U.S. patent application number 10/017221 was filed with the patent office on 2002-06-27 for method and system for determining analyte activity.
This patent application is currently assigned to California Institute of Technology. Invention is credited to Lewis, Nathan S., Vaid, Thomas P..
Application Number | 20020081232 10/017221 |
Document ID | / |
Family ID | 22166363 |
Filed Date | 2002-06-27 |
United States Patent
Application |
20020081232 |
Kind Code |
A1 |
Lewis, Nathan S. ; et
al. |
June 27, 2002 |
Method and system for determining analyte activity
Abstract
C-hemical sensors for detecting the activity of a molecule or
analyte of interest is provided. The chemical sensors comprise and
array or plurality of chemically-sensitive resistors that are
capable of interacting with the molecule of interest, wherein the
interaction provides a resistance fingerprint. The fingerprint can
be associated with a library of similar molecules of interest to
determine the molecule's activity.
Inventors: |
Lewis, Nathan S.; (La
Canada, CA) ; Vaid, Thomas P.; (St. Louis,
MO) |
Correspondence
Address: |
GARY CARY WARE & FRIENDENRICH LLP
4365 EXECUTIVE DRIVE
SUITE 1600
SAN DIEGO
CA
92121-2189
US
|
Assignee: |
California Institute of
Technology
|
Family ID: |
22166363 |
Appl. No.: |
10/017221 |
Filed: |
December 13, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10017221 |
Dec 13, 2001 |
|
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09291932 |
Apr 13, 1999 |
|
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60081781 |
Apr 14, 1998 |
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Current U.S.
Class: |
422/82.02 ;
422/67; 422/68.1; 422/98; 435/287.9; 435/6.11 |
Current CPC
Class: |
B01J 2219/00725
20130101; B01J 2219/00659 20130101; C40B 40/10 20130101; B01J
2219/00637 20130101; B01J 2219/007 20130101; B01J 2219/00605
20130101; B01J 2219/0072 20130101; B01J 2219/00722 20130101; G01N
27/126 20130101; B01J 2219/00612 20130101; G01N 33/54373 20130101;
B01J 2219/00707 20130101; C40B 40/06 20130101; B01J 2219/00689
20130101 |
Class at
Publication: |
422/82.02 ;
422/68.1; 422/67; 435/6; 422/98; 435/287.9 |
International
Class: |
C12M 001/34; G01N
027/00 |
Claims
We claim:
1. An analyte screening system, comprising: a sensor array
comprising a plurality of different differentially responsive
sensors, having a first signal profile produced by the plurality of
different differentially responsive sensors, when contacted with a
first analyte and a second different signal profile produced when
contacted with a second analyte, wherein the difference between the
first signal and the second signal being indicative of a difference
in the property or properties of the first analyte and second
analyte; a measuring device, connected to the sensor array; and a
computer; the measuring device detecting a signal in each of the
plurality of different differentially responsive sensors and the
computer assembling the signal into a sensor array signal profile;
wherein the computer is operative to compare the sensor array
signal profile to at least one previously obtained signal profile
indicating a standard sample having a specific activity, chemical
or physical property, or function, wherein the comparison of the
sensor_array signal profile to the at least one previously obtained
signal profile is indicative of a specific activity, chemical or
physical property, or function of the analyte.
2. The system of claim 1, wherein the analyte comprises a
chemical.
3. The system of claim 2, wherein the analyte comprises a
chemical.
4. The system of claim 3, wherein the biochemical is selected from
the group consisting of a lipid, hormone, fatty acids, nucleic
acid, polypeptide, and carbohydrate.
5. The system of claim 4, wherein the polypeptide is selected from
the group consisting of an antibody, enzyme, and protein.
6. The system of claim 5, wherein the antibody is a monoclonal
antibody, polyclonal antibody, humanized antibody, or fragments
thereof.
7. The system of claim 5, wherein the enzyme is selected from the
group consisting of lipases, esterases, proteases, glycosidases,
glycosyl transferases, phosphateses, kinases, mono- and
dioxygenases, haloperoxidases, lignin peroxidases, diarylpropane
peroxidases, eposide hydrolases, nitrile hydrotases, nitrilases,
transaminases, amidases, and acylases.
8. The system of claim 1, wherein the specific activity is selected
from the group consisting of enzymatic activity, binding activity,
inhibitory activity, and modulating activity;
9. The system of claim 1, wherein the signal profile of the
standard sample is derived from a library.
10. The system of claim 9, wherein the library is generated by a
neural network.
11. The system of claim 1, wherein the different differentially
responsive sensors change optically, electrically, magnetically,
mechanically, physically, or a combination thereof.
12. The system of claim 1, wherein the different differentially
responsive sensors are selected from the group consisting of
crystalline colloidal array (CCA) containing sensors, metal oxide
sensors, dye-impregnated polymers coated onto beads of optically
fibers, buld conducting organic polymers, capacitance sensors,
chemically-sensitive resistor sensors, and combinations
thereof.
13. The system of claim 12, wherein the chemically-sensitive
resistor sensors are comprised of regions of a non-conductive
material and regions of a conductive material compositionally
different than the non-conductive material, each resistor providing
an electrical path through the regions of conductive and
non-conductive material, wherein interaction of the molecule with
the resistor provides a change in resistance in the resistor.
14. The system of claim 1, wherein the chemical or physical
property is selected from the group consisting of side groups,
charge, hydrophobicity, polarity, molecular size or shape, and
chirality.
15. The system of claim 1, wherein the different differentially
responsive sensors are chemically sensitive resistors.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation and claims the benefit of
priority under 35 U.S.C. .sctn.120 of U.S. patent application Ser.
No. 09/291,932, filed on Apr. 13, 1999 which claims the benefit
under 35 U.S.C. .sctn.119(e)(1) to U.S. Provisional Application No.
60/081,781, filed on Apr. 14, 1998, which is incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] The present invention relates to a sensor apparatus useful
in detecting trace analytes in a sample, and more specifically
determining the analyte's biological or physical activity.
BACKGROUND OF THE INVENTION
[0003] There exists a need for determining a variety of molecular
properties that are important in determining a biological, a
chemical, or a physical property or activity of a molecule and
cataloging these properties so that they can be used to identify
candidate lead molecules for a biological, chemical,
pharmaceutical, or industrial application of interest.
[0004] Currently methods are used to screen potential drug
compounds or biologics from a collection of molecules of interest
(e.g., a library). Such methods use assay techniques that detect a
specific activity based on a molecule's binding affinity, enzymatic
activity or other properties. Alternatively, lead compounds are
generated by computational methods, wherein the molecules that
possess certain desirable properties are defined by shape, dipole
moments, surface area, solubility, vapor pressure, hydrophobicity,
hydrophilicity, antigenicity and other physical properties. These
chemical-physical properties are then defined and used to
computationally narrow lead compounds to a manageable subset which
are then analyzed further by additional screening techniques
designed to measure a specific activity in vitro or in vivo by
using additional high throughput screening techniques.
[0005] Generation of lead compounds is important because not only
does it allow for exploration of a wider range of potential
pharmaceutical agents, but it also offers opportunities for
construction of follow-up libraries that focus on the molecular
characteristics represented by these lead molecules. This in turn
is performed to provide yet more leads with the desired
pharmaceutical activity eventually with the hope of finding a
candidate suitable for clinical use.
SUMMARY OF THE INVENTION
[0006] The present invention provides a method for identifying a
specific activity, structure or function of a molecule of interest
based on a sensing device. The sensing device includes an array of
sensors responsive to a molecule's physical, chemical, or
biological characteristics. The differentially responsive sensors
can be optical sensors, resonance mechanic frequency sensors,
and/or electrical sensors to name a few. Other sensors and arrays
are known to those of skill in the art. For example, in one
embodiment the sensing device includes a chemical sensor comprising
first and second conductive elements (e.g. electrical leads)
electrically coupled to a chemically sensitive resistor which
provides a selective electrical path between the conductive
elements. The resistor comprises a plurality of alternating
non-conductive regions (comprising a non-conductive material) and
conductive regions (comprising a conductive material) in series.
The electrical path between the first and second conductive
elements is transverse to (i.e., passes through) a plurality of
alternating non-conductive and conductive regions. In use, the
resistor provides a change in resistance between the conductive
elements when contacted with an analyte or molecule which interacts
with the non-conductive region. The non-conductive region can be
made of any material designed to interact or bind to a class,
genus, or specie of analyte.
[0007] The disclosure provides a method and device for identifying
a specific activity, structure or function of an analyte or
molecule of interest. The method uses a sensing device to produce a
characteristic experimental pattern generated by a plurality of
differentially responsive sensors. The pattern has information on
the desired molecular properties for a molecule or analyte of
interest. A response pattern is produced for each member of the
library. These patterns are then stored and associated with the
library. The library contains patterns for molecules having a
desired or known property or activity.
[0008] In one embodiment, a method is provided for screening
samples for a specific activity or structure by measuring outputs
of a plurality of chemically-sensitive resistors, each resistor
comprising a conductive material and a non-conductive material;
using results of said measuring to obtain a signal profile,
relating to a change in resistance in the plurality of resistors;
and comparing the signal profile to a previously-obtained signal
profile indicating a standard sample having a specific activity,
wherein the signal profile is indicative of a specific activity or
a specific structure.
[0009] The disclosure additionally provides a screening system that
includes a sensor array comprising a plurality of differentially
responsive sensors, each sensor capable of providing a signal
corresponding to the sensors interaction with a molecule of
interest. A measuring device detects the signal from each sensor
and arranges them into a signal profile representing a molecule's
characteristics (e.g., activity, structure, or function). A
computer then compares the signal profile to determine the
molecule's activity. Preferably, the computer has a resident
algorithm for comparing the signal profile(s).
[0010] For example, in one embodiment, a sample screening system is
provided, the system including a sensor array comprising at least
first and second chemically-sensitive resistors, each
chemically-sensitive resistor comprising a mixture of
non-conductive organic polymer and conductive material
compositionally different than said non-conductive organic polymer,
each resistor providing an electrical path through said mixture of
non-conductive organic polymer and said conductive material, a
first electrical resistance, when contacted with a first chemical
analyte at a first concentration and a second different electrical
resistance when contacted with a second analyte, wherein the
difference between the first electrical resistance and the second
electrical resistance of the first chemically-sensitive resistor
being different from the difference between the first electrical
resistance and the second electrical resistance of the second
chemically-sensitive resistor; an electrical measuring device
electrically connected to the sensor array; and a computer wherein
the electrical measuring device detects the first and second
electrical resistance in each of the chemically-sensitive resistors
and the computer assembles the resistance into a sensor array
signal profile, wherein the computer is operative to compare the
signal profile to a signal profile obtained from a standard sample
having a specific activity, wherein the signal profile is
indicative of a specific activity or a specific structure.
BRIEF DESCRIPTION OF THE DRAWING
[0011] These and other objects of the present invention will now be
described in detail with reference to the accompanying drawing, in
which:
[0012] FIG. 1A shows an overview of sensor design; 1B, shows an
overview of sensor operation; and 1C, shows an overview of system
operation.
[0013] FIG. 2 presents the relative differential resistance
responses for various conducting polymer composite sensors to three
representative alcohols.
[0014] FIG. 3 shows a plot of pI.sub.50 predicted by equation 3
versus the actual experimental value. Horizontal error bars
represents an average experimental error and vertical error bars
correspond to the standard error of equation 3. The line represents
perfect agreement between experiment and prediction.
[0015] FIG. 4 shows a diagram illustrating the M and A steric
parameters.
[0016] FIGS. 5A-5D show a table where the first three columns give
the name of the alcohol, its experimental pI.sub.50 value and run
in which it was analyzed (and the bubbler in which it was placed).
The remainder of the table lists the responses (expressed as
percent change in electrical resistance relative to base line
resistance) of the 19 different polymer/carbon black sensors upon
exposure to the alcohols at 5% of their respective saturated vapor
pressures. The standard deviation of the responses over ten trials
are given in
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0017] The approach described herein uses experimental data (e.g. a
signal profile, such as a resistance fingerprint) that is generated
by an array of differentially responsive sensors. Such sensors
include, for example, chemically-sensitive resistor of a sensing
array, such as that found in an "electronic nose" as described in
U.S. Pat. No. 5,571,401 (the disclosure of which is incorporated
herein), when it is exposed to a molecule of interest. The change
in the electrical resistance of a chemically-sensitive resistor in
such a sensing array can be related to the sorption of a molecule
of interest to the non-conductive regions of the
chemically-sensitive resistor. The signals produced by a plurality
of chemically-sensitive resistors having individual sorption
criteria thus provide information on a number of chemically
important properties, such as the hydrophobicity, molecular size,
polarity, and hydrogen-bonding interactions of a molecule of
interest, thus, for example, creating a resistance profile or
fingerprint of the molecule of interest based upon its chemical
properties.
[0018] Another type of sensor, includes, for example, hydrogels
containing a crystalline colloidal array (CCA) as disclosed in U.S.
Pat. No. 5,854,078 (the disclosure of which is incorporated herein
by reference). Such hydrogels undergo a volume change in response
to a specific chemical species. As the hydrogels are modulated in
size the lattice spacing of the CCA embedded therein changes as
well. The light diffraction, therefore, indicates the presence or
absence of the stimuli that causes the volume of the hydrogel to
change.
[0019] Yet another type of sensor includes those disclosed in U.S.
Pat. No. 5,512,490 to Walt et al. (the disclosure of which is
incorporated herein by reference). The optic sensor of this system
is comprised of a supporting member and an array formed of
heterogeneous, semi-selective thin films which function as sensing
receptor units and are able to detect a variety of different
analytes and ligands using spectral recognition patterns. Each
formulation of sensing receptor unit comprising the array of the
optical sensor reacts with a plurality of different chemical
compounds and compositions; and for each individual chemical
compound, provides a spectral response pattern over time (by
changes in energy intensity, or by changes in wavelength or both of
these parameters) which is indicative of the event and consequence
of the reaction with a single compound. The array also generates
spectral responses and patterns from mixtures of different
compounds based upon the optical responses from each of the
individual compounds forming this mixture.
[0020] By "molecule of interest" or "analyte" is meant any number
of various molecules. For example a molecule or analyte of interest
may be a nucleic acid (e.g., DNA or RNA), a polypeptide (e.g., an
antibody, protein, enzyme), a biochemical (e.g., a lipid, hormone,
fatty acids, carbohydrate), pharmaceuticals, a chemical such as
organics including, for example, alkanes, alkenes, alkynes, dienes,
alicyclic hydrocarbons, arenes, alcohols, ethers, ketones,
aldehydes, cyclic hydrocarbons, carbonyls, carbanions, polynuclear
aromatics and derivatives of such organics, e.g., halide
derivatives.
[0021] By "differentially responsive sensors" is meant any number
of sensors that respond to the presence or interaction of a
collection of molecules with the sensor by providing some
measurable change. Each individual sensor does not uniquely probe
the property of interest, thus any individual signal alone is not
sufficient to determine the desired chemical or biological property
of an analyte. Instead the response pattern of a plurality of
sensors is used to obtain the desired activity through comparison
with a standard response pattern produced by an analyte with a
known activity. Such measurable changes include changes in optical
wavelengths, transparency of a sensor, resonance of a sensor,
resistance, diffraction of light and/or sound, and other changes
easily identified to those skilled in the art. Such sensors
include, but are not limited to, crystalline colloidal array (CCA)
sensors and variants, such as hydrogels containing CCA, metal oxide
sensors, dye-impregnated polymers coated onto beads or optical
fibers, bulk conducting organic polymers, and capacitance
sensors.
[0022] In a traditional assay method, a desired chemical or
biological activity is determined by the response of one designed
sensor or interest. This single response is known to probe the
chemical and/or biological activity of interest, and the magnitude
of the sensor response is then readily and directly related to the
activity of concern. For instance, enzymatic inhibition by a
certain ligand could be determined from an assay that probed
directly the amount of substrate consumed by the enzyme under
various conditions or indirectly by the amount of product
metabolized under known and calibrated conditions. Different
ligands would produce different amounts of substrate consumption or
metabolite products, and the amounts of such would directly
indicate the desired biological property, the ligand inhibition of
the enzymatic activity. Another example would be determination of
the presence of a particular nucleic acid sequence in a sample
through investigating the response of array of sensors, each of
which had complements to different but known or knowable sequences
of nucleic acids. The sensor that displayed the highest response
change (for instance, florescence appearance or disappearance,
electrochemical activity or disappearance of electrochemical
activity, etc.) would then be uniquely associated with the presence
of the sequence of interest in the sample, through knowledge of the
complementary sequence that was present on that particular sensing
element and association of the knowledge of the complementary
sequence with the sequence that must then be present in the analyte
of interest.
[0023] The present invention utilizes a different approach. A
plurality of differentially responsive sensors, each of which
provides measurable signals in response to a variety of analytes,
chemicals, and biochemicals of concern, is used. The desired
chemical or biological activity is not revealed by the response of
an individual sensor or individual sensor response signal, but is
instead obtained by pattern analysis of the responses produced by a
plurality of differentially responsive sensors in the sensor array
device. The sensors may or may not themselves be selective and
predetermined to uniquely probe the chemical or biological property
of interest, but the various differing response patterns produced
by the analytes of interest upon exposure to the plurality of
sensors is correlated with the desired chemical and/or biological
activity after comparison of the response pattern to the pattern
produced by an analyte with a known chemical and/or biological
activity.
[0024] By allowing interactions between a molecule of interest and
a differentially responsive sensor, the signals can be directly or
indirectly related to the properties of the molecule of interest.
For example, by using a chemically-sensitive resistor in an array
of an "electronic nose" it is possible to directly and indirectly
relate the signals of the electronic nose to the properties of the
molecules of interest. For example, if the significant interactions
between a molecule and the binding site of an enzyme are related
either directly or indirectly in the collection of binding
constants of that molecule to a non-conductive element of the
electronic nose, then it is possible to relate the electronic nose
signal to the enzyme's binding properties.
[0025] Once a signature of a set of molecules has been obtained,
the signature profile could be used, with an appropriate training
set to predict the activity of any member of the library in a
chemical interaction of interest.
[0026] As described in the Example below, which is not meant to
limit the present claims, the inventor has demonstrated that an
electronic nose is capable of identifying alcohols having chemical
characteristics that are capable of inhibiting cytochrome P-450
activity. Such chemical characteristics are related to various
chemical-physical parameters of the alcohol including its
three-dimensional structure, side groups, charge, and other
parameters known to those of skill in the art.
[0027] The methods and apparatus of the present invention are
applicable to a wide range of molecules of interest and types of
sensors and arrays. For example, and not by way of limitation, one
embodiment provides a method which can be used to identify
polypeptides having a biological function. Such functions include a
polypeptide's role as a receptor, receptor antagonist or agonist,
enzymatic activity (e.g., lipases, esterases, proteases,
glycosidases, glycosyl transferases, phosphatases, kinases, mono-
and dioxygenases, haloperoxidases, lignin peroxidases,
diarylpropane peroxidases eposide hydrolases, nitrile hydrotasees,
nitrilases, transaminases, amidases and acylases), DNA-binding
ability (e.g., histones), antibody activity (e.g., the ability to
bind an epitope), such antibodies include monoclonal, polyclonal
and humanized antibodies to name a few. The activity can be
determined based on the polypeptides primary, secondary, and/or
tertiary structure as well as its charge, hydrophobicity,
hydrophilicity and other polypeptide properties known to those of
skill in the art as compared to a library of similar polypeptide
molecules. The differentially responsive sensors (e.g., a
chemically-sensitive resistor) of an array for detection of such
properties are designed as described herein or in any number of
ways known to those of skill in the art. The differentially
responsive sensors do not need to be specially designed to bind a
specific polypeptide. For example, where the sensor is of a
resistor-type, the elements do not need to be specially designed to
bind such polypeptides so long as they are capable of detecting
properties by interactions of the polypeptide or molecule of
interest with the chemically-sensitive resistor.
[0028] In another embodiment, the present invention provides a
method of detecting the activity of a biological molecule or
pharmaceutical compound. The method provides contacting a plurality
of differentially responsive sensors with a compound or molecule of
interest and then comparing the "fingerprint" (e.g., a resistance
fingerprint) or "profile" with the fingerprint of other related
molecules having a desired activity or function. Molecules having
similar fingerprints are indicative of molecules having similar
activities. Such activities can range from the detection of disease
molecules (e.g., viral antigens, bacterial antigens, such as LPS,
endotoxin, etc.), carcinogenic molecules, antibiotic molecules,
antiviral molecules, viral compounds and any number of molecules
now known or discovered, so long as they are capable of interacting
with a plurality of sensors thus eliciting a change in for example,
optics, resonance, and/or current across the sensor (e.g., increase
or decrease in the resistance).
[0029] Learning based and/or pattern-recognition based algorithms
are used to identify leads from the library based on the data
contained in the experimental response patterns, without the need
necessarily for additional assays or for additional computations on
the remaining members of the library. Additionally, one advantage
of the invention is that it provides an experimentally based
measure of the molecular properties involved in the desired binding
event. Once a pattern has been recorded for a library, it remains
associated with that library indefinitely and can be used for other
purposes subsequently. For example, after screening a library for
leads in activity towards a given binding site, with a few new
examples on another binding site, the patterns can then be
interrogated again to produce leads for this new target event
without the need to recollect the response patterns nor to reassay
the entire library for activity towards that particular new
process.
[0030] The differentially responsive sensors in the array need not
be carefully tailored towards the molecule of interest. Instead, it
is sufficient that they collectively probe a broad range of
molecular properties, for example, hydrophobicity, polarity,
molecular size or shape, chirality, and other chemical-physical
characteristics known in the art. Each individual sensor need not
selectively probe these properties, nor is it essential that the
experimentalist evaluate in advance which properties are being
probed by the array. For example, an array may have any number of
responsive sensors from one to greater than 10.sup.6 In a preferred
embodiment, wherein the sensors are resistors the array would have
a significant (>10) number of chemically-sensitive detectors or
resistors, each of which would be at least partially responsive to
certain properties that affect molecular binding and recognition
events.
[0031] The signal transduction mechanism through which the analyte
or molecule produces an array response or resistance fingerprint is
potentially quite broad. Many methods are known to those skilled in
the art of constructing artificial nose devices. These include
arrays of surface acoustic wave devices, quartz crystal
micro-balances, dye-coated fiber optics, conducting organic
polymers, electrochemical gas sensors, fiber optic micromirrors,
composites of insulating organic polymers and conductors, tin oxide
sensors, hydrogel based CCA, nucleic acid or protein based polymers
(see for example Ramsey, Graham, Nature Biotech, 16:40-44, (1998)),
and others readily identifiable to those skilled in the art.
Different types of signal transduction mechanisms could also be
used in one array to expand the information contained in the
response pattern produced by the analyte or molecule of interest,
for example optical, electrical, and/or resonance.
[0032] When the differentially responsive sensor is a resistor, the
resistor comprises a plurality of alternating non-conductive and
conductive regions transverse to an electrical path between
conductive leads. Generally, the resistors are fabricated by
blending a conductive material with a non-conductive material such
that the electrically conductive path between the leads coupled to
the resistor is interrupted by gaps of non-conductive material. For
example, in a colloidal suspension or dispersion of particulate
conductive material in a matrix of non-conductive material, the
matrix regions separating the particles provide the gaps. The
non-conductive gaps range in path length from about 10 to 1,000
angstroms, usually on the order of 100 angstroms providing
individual resistance of about 10 to 1,000 m.OMEGA., usually on the
order of 100 m.OMEGA., across each gap. The path length and
resistance of a given gap is not constant but rather is believed to
change as the nonconductive organic polymer of the region absorbs,
adsorbs or imbibes an analyte. Accordingly the dynamic aggregate
resistance provided by these gaps in a given resistor is a function
of analyte permeation of the non-conductive regions. However, it
will be recognized that materials which change conformationally, or
effect a proton distribution or availability, in response to the
binding of an analyte are also encompassed by the present
disclosure. For example, a non-conductive material which results in
a proton change upon binding of an analyte can cause an exponential
change in the resistance of the chemically-sensitive resistor. In
some embodiments, the conductive material may also contribute to
the dynamic aggregate resistance as a function of analyte
permeation (e.g., when the conductive material is a conductive
material such as a polypryole).
[0033] A wide variety of conductive materials and non-conductive
materials can be used. Table 1 provides exemplary conductive
materials for use in resistor fabrication; mixtures, such as of
those listed, may also be used. Table 2 provides exemplary
non-conductive materials; blends and copolymers, such as the
materials listed here, may also be used. Combinations,
concentrations, blend stoichiometries, percolation, threshold, etc.
are readily determined empirically by fabricating and screening
prototype resistors (chemiresistors) as described below.
1TABLE 1 Major Class Examples Organic conducting polymers
(poly(anilines), poly(thiophenes), Conductors poly(pyrroles),
poly(aceylenes, etc.)), carbnaceious material (carbon blacks,
graphite, coke, C60 etc.), charge transfer complexes
(tetramethylparaphnylene- diamine-chloranile, alkaili metal
tetracyanoquino- dimethane complexes, tetrathiofulvalene halide
complexes, etc.), etc. Inorganic metals and metal alloys (Ag, Au,
Cu, Pt, AuCu alloy, Conductors etc.), highly doped semiconductors
(Si, GaAs, InP, MoS2, TiO2, etc.), conductive metal oxides (In2O3,
SnO2, Na2Pt3O4, etc.), superconductors (Yba2Cu3O7, Ti2Ba2Ca2Cu3O10,
etc.), etc. Mixed Tetracyanoplatinate complexes, Iridium
halocarbonyl inorganic/organic complexes, stacehed macrocyclic
complexes. Etc. Conductor
[0034]
2TABLE 2 Major Class Examples Main-chain poly(dienes),
poly(alkenes), poly(acrylics), carbon polymers poly(methacrylics),
poly(vinyl ethers), poly(vinyl thioethers), poly(vinyl alcohols),
poly(vinyl ketones), poly(vinyl halides), poly(vinyl nitrites),
poly(vinyl esters), poly(styrenes), poly(aryines), etc. Main-chain
poly(oxides), poly(caronates), poly(esters), acyclic
poly(anhydrides), poly(urethanes), poly(sulfonate), heteroatom
poly(siloxanes), poly(sulfides), poly(thioesters), polymers
poly(sulfones), poly(sulfonamindes), poly(amides), poly(ureas),
poly(phosphazens), poly(silanes), poly(silazanes), etc. Main-chain
poly(furantetracarboxylic acid diimides), heterocyclic
poly(benzoxazoles), poly(oxadiazoles), polymers
poly(benzothiazinophenothiazines), poly(benzothiazoles),
poly(pyrazinoquinoxalines), poly(pyromenitimides),
poly(quinoxalines), poly(benzimidazoles), poly(oxidoles),
poly(oxoisinodolines), poly(diaxoisoindoines), poly(triazines),
poly(pyridzaines), poly(pioeraziness), poly(pyridinees),
poly(pioeridiens), poly(triazoles), poly(pyrazoles),
poly(pyrrolidines), poly(carboranes), poly(oxabicyclononanes),
poly(diabenzofurans), poly(phthalides), poly(acetals),
poly(anhydrides), carbohydrates, etc.
[0035] The chemiresistors can be fabricated by many techniques such
as, but not limited to, solution casting, suspension casting, and
mechanical mixing. In general, solution case routes are
advantageous because they provide homogenous structures and ease of
processing. With solution case routes, resistor elements may be
easily fabricated by spin, spray or dip coating. Since all elements
of the resistor must be soluble, however, solution case routes are
somewhat limited in their applicability. Suspension casting still
provides the possibility of spin spray or dip coating but more
heterogeneous structures than with solution casting are expected.
With mechanical mixing, there are fewer solubility restrictions
since it involves only the physical mixing of the resistor
components but device fabrication is more difficult since spin,
spray and dip coating are no longer possible. A more detailed
discussion of each of these follows.
[0036] For systems where both the conducting and non-conducting
media or their reaction precursors are soluble in a common solvent,
the chemiresistors can be fabricated by solution casting. The
oxidation of pyrrole by phosphomolybdic acid presented herein
represents such a system. In this reaction, the phosphomolybdic
acid and pyrrole are dissolved in tetrahydrofuran (THF) and
polymerization occurs upon solvent evaporation. This allows for THF
soluble non-conductive polymers to be dissolved into this reaction
mixture thereby allowing the blend to be formed in a single step
upon solvent evaporation. The choice of non-conductive material in
this route is, of course, limited to those that are soluble in the
reaction media. For the poly(pyrrole) case described above,
preliminary reactions were performed in THF, but this reaction
should be generalizable to other non-aqueous solvent such as
acetonitrile or ether. A variety of permutations on this scheme are
possible for other conducting material. Some of these are listed
below. Certain conducting materials, such as substituted
poly(cyclooctatetraenes- ), are soluble in their undoped,
non-conducting state in solvents such as THF or acetonitrile.
Consequently, the blends between the undoped material and
plasticizing material can be formed from solution casting. After
which, the doping procedure (exposure to I.sub.2 vapor, for
instance) can be performed on the blend to render the substituted
poly (cyclooctatetraene) conductive. Again, the choice of
non-conductive materials is limited to those that are soluble in
the solvents that the undoped conducting material is soluble in and
to those stable to the doping reaction. Certain conducting
materials can also be synthesized via a soluble precursor material.
In these cases, blends between the precursor material and the
non-conducting material can first be formed followed by chemical
reaction to convert the precursor material into the desired
conducting material. For instance poly(.rho.-phenylene vinylene)
can be synthesized through a soluble sulfonium precursor. Blends
between this sulfonium precursor and the non-conductive material
can be formed by solution casting. After which, the blend can be
subjected to thermal treatment under vacuum to convert the
sulfonium precursor to the desired poly(.rho.-phenylene
vinylene).
[0037] In suspension casting, one or more of the components of the
resistor is suspended and the others dissolved in a common solvent.
Suspension casting is a rather general technique applicable to a
wide range of species, such as carbon blacks or colloidal metals,
which can be suspended in solvents by vigorous mixing or
sonication. In one application of suspension casting, the
non-conductive material is dissolved in an appropriate solvent
(such as THF, acetonitrile, water, etc.). Colloidal silver is then
suspended in this solution and the resulting mixture is used to dip
coat electrodes.
[0038] Mechanical mixing is suitable for all of the
conductive/non-conductive combinations possible. In this technique,
the materials are physically mixed in a ball-mill or other mixing
device. For instance, carbon black: non-conductive material
composites are readily made by ball-milling. When the
non-conductive material can be melted or significantly softened
without decomposition, mechanical mixing at elevated temperature
can improve the mixing process. Alternatively, composite
fabrication can sometimes be improved by several sequential heat
and mix steps. Once fabricated, the individual elements can be
optimized for a particular application by varying their chemical
make up and morphologies. The chemical nature of the resistors
determines to which analytes they will respond and their ability to
distinguish different analytes. The relative ratio of conductive to
insulating components determines the magnitude of the response
since the resistance of the elements becomes more sensitive to
sorbed molecules as the percolation threshold is approached. The
film morphology is also important in determining response
characteristics. For instance, thin films respond more quickly to
analytes than do thick ones. Hence, with an empirical catalogue of
information on chemically diverse sensors made with varying ratios
of insulating to conducting components and by differing fabrication
routes, sensors can be chosen that are appropriate for the analytes
expected in a particular application, their concentrations, and the
desired response times. Further optimization can then be performed
in an iterative fashion as feedback on the performance of an array
under particular conditions becomes available.
[0039] The resistor may itself form a substrate for attaching the
lead or the resistor. For example, the structural rigidity of the
resistors may be enhanced through a variety of techniques:chemical
or radiation cross-linking of polymer components (dicumyl peroxide
radical cross-linking, UV-radiation cross-linking of poly(olefins),
sulfur cross-linking of rubbers, e-beam cross-linking of Nylon,
etc.), the incorporation of polymers or other materials into the
resistors to enhance physical properties (for instance, the
incorporation of a high molecular weight, high transition metal
(Tm) polymers), the incorporation of the resistor elements into
supporting matrices such as clays or polymer networks (forming the
resistor blends within poly-(methylmethacrylate) networks or within
the lamellae of montmorillonite, for instance), etc. In another
embodiment, the resistor is deposited as a surface layer on a solid
matrix which provides means for supporting the leads. Typically,
the matrix is a chemically inert, non-conductive substrate such as
a glass or ceramic.
[0040] Sensor arrays particularly well-suited to scaled up
production are fabricated using integrated circuit (IC) design
technologies. For example, the chemiresistors can easily be
integrated onto the front end of a simple amplifier interfaced to
an A/D converter to efficiently feed the data stream directly into
a neural network software or hardware analysis section.
Micro-fabrication techniques can integrate the chemiresistors
directly onto a micro-chip which contains the circuitry for analog
signal conditioning/processing and then data analysis. Ink-jet
technology can be used for the production of millions of
incrementally-different sensor elements in a single manufacturing
step. Controlled compositional gradients in the chemiresistor
elements of a sensor array can be induced in a method analogous the
way that a color ink-jet printer deposits and mixes multiple
colors. However, in this case, rather than multiple colors, a
plurality of different polymers in solution which can be deposited
are used. A sensor array of a million distinct elements only
requires a 1 cm.times.1 cm sized chip employing lithography at the
10 .mu.m feature level, which is within the capacity of
conventional commercial processing and deposition methods. This
technology permits the production of sensitive, small-sized,
stand-alone chemical sensors.
[0041] Preferred sensor arrays have a predetermined inter-sensor
variation in the structure or composition of the non-conductive
polymer regions. The variation may be quantitative and/or
qualitative. For example, the concentration of the non-conductive
material in the blend can be varied across sensors. Alternatively,
a variety of different materials may be used in different sensors.
In one embodiment, an electronic nose for detecting an analyte in a
sample is fabricated by electrically coupling the sensor leads of
an array of compositionally different sensors to an electrical
measuring device. The device measures changes in resistivity at
each sensor of the array, preferably simultaneously and preferably
over time. Frequently, the device includes signal processing means
and is used in conjunction with a computer and data structure for
comparing a given response profile to a structure-response profile
database for qualitative and quantitative analysis. Typically such
a nose comprises at least ten, usually at least 100, and often at
least 1000 different sensors though with mass deposition
fabrication techniques described herein or otherwise known in the
art, arrays of on the order of at least 10.sup.6 sensors are
readily produced.
[0042] In operation, each resistor provides a first electrical
resistance between its conductive leads when the resistor is
contacted with a first sample comprising a chemical analyte at a
first concentration, and a second electrical resistance between its
conductive leads when the resistor is contacted with a second
sample comprising the same chemical analyte at a second different
concentration. The samples may be liquid or gaseous in nature. The
first and second samples may reflect samples from two different
environments, a change in the concentration of an analyte in a
sample sampled at two time points, a sample and a negative control,
etc. The sensor array necessarily comprises sensors which respond
differently to a change in an analyte concentration, i.e. the
difference between the first and second electrical resistance of
one sensor is different from the difference between the first and
second electrical resistance of another sensor.
[0043] In a preferred embodiment, the temporal response of each
sensor (resistance as a function of time) is recorded. The temporal
response of each sensor may be normalized to a maximum percent
increase and percent decrease in resistance which produces a
response pattern associated with the exposure of the analyte. By
iterative profiling of known analytes, a structure-function
database correlating analytes and response profiles is generated.
Unknown analyte may then be characterized or identified using
response pattern comparison and recognition algorithms.
Accordingly, analyte detection systems comprising sensor arrays, an
electrical measuring device for detecting resistance across each
chemiresistor, a computer, a data structure of sensor array
response profiles, and a comparison algorithm are provided. In
another embodiment, the electrical measuring device is an
integrated circuit comprising neural network-based hardware and a
analog-digital converter (ADC) multiplexed to each sensor, or a
plurality of ADCs, each connected to different sensor(s).
[0044] A wide variety of analytes and samples may be analyzed by
the disclosed sensors, arrays and noses so long as the subject
analyte is capable of generating a differential response across a
plurality of sensors of the array. Analyte applications include
broad ranges of chemical classes such as organics such as alkanes,
alkenes, alkynes, dienes, alicyclic hydrocarbons, arenes, alcohols,
ethers, ketones, aldehydes, carbonyls, carbanions, polynuclear
aromatics and derivatives of such organics, e.g. halide
derivatives, etc., biomolecules such as sugars, isoprenes and
isoprenoids, fatty acids and derivatives, etc. Accordingly,
commercial applications of the sensors, arrays and noses include
environmental toxicology and remediation, biomedicine, materials
quality control, food and agricultural products monitoring,
etc.
[0045] The general method for using the disclosed sensors, arrays
and electronic noses, for detecting the characteristics or presence
of an analyte in a sample involves sensing a change in a
differentially responsive sensor to the presence of an analyte in a
sample. For example, where the sensor is a resistor-type sensor,
measurement in resistance changes where the chemical sensor
comprises first and second conductive leads electrically coupled to
and separated by a chemically-sensitive resistor as described above
by measuring a first resistance between the conductive leads when
the resistor is contacted with a first sample comprising an analyte
at a first concentration and a second different resistance when the
resistor is contacted with a second sample comprising the analyte
at a second different concentration.
[0046] In one embodiment, a rapid method for individually
addressing the members of the library and individually collecting
their response patterns is desirable. In this embodiment, gases
derived from a fluid are used wherein the analytes are vapors that
are to be detected by their response on the sensor array. A
substrate contains the library (e.g., analyte matrix) of interest
whose response patterns are to be collected. The substrate may be
cooled in order to reduce the vapors emanating from the molecules
before analysis. The sensors (e.g. "chemically-sensitive
resistors") are also placed within this analysis chamber. Either a
carrier gas or a vacuum is present in order to insure that the
chamber is not contaminated with residual molecules of a prior
analysis, inlet and outlet ports may be used to manipulate and
control the gas flow. The sensors themselves may also have a
temperature control, as may the walls of the chamber. Cooling the
walls of the chamber will prevent desorption of the impurities
during an analysis and heating can be used to clean the chamber of
such impurities at a subsequent time. Temperature control of the
sensors is beneficial to control the sensitivity, response time,
and noise characteristics of the detectors in the sensor array.
[0047] In this embodiment, the library is cooled so that the vapor
pressure of each individual molecular constituent is maintained at
a background level until that particular species is to be analyzed.
When a response pattern is to be recorded for a molecule of
interest, the region that contains the molecule of interest in the
library is heated to volatilize it. This thereby produces vapors
that can be transported to the detectors of the sensor array. This
local heating could be performed by a laser spot, by an addressable
resistive grid of wires that contacts the substrate or a portion of
the substrate, or by other methods of heating that are known to
those skilled in the art of generating temperature excursions in
materials. After the pattern is recorded for the spot of interest,
the temperature is returned to its set point and another spot is
interrogated. An alternative uses multiple sets of detectors in
parallel, with accompanying stimuli in parallel, to increase the
throughput of analysis of an entire array if so desired.
[0048] Once a series of patterns has been collected, some initial
information is desirable in order to identify leads for a
particular activity or function. For example, experimental data
might be available showing that members 1, 2, and 3 have increasing
activity towards the activity or function of interest. Such
activity or function may be derived from known molecules whose
activity has been well-characterized or may be subsequently or
contemporaneously measured by additional assay techniques. Analysis
of the patterns produced by the electronic nose array using neural
network or statistical method-based programs would then identify
which molecules, or which molecular properties being probed by the
detectors, showed a correlation with the specific or desired
activity or function. Once this correlation is established,
analysis of the remaining patterns would be used to identify
molecules with similar correlation, and thereby to identify the
leads in the library for further interrogation and analysis both
experimentally and theoretically, as appropriate.
[0049] The analysis of a resistance signal pattern (e.g. a
resistance profile) of the embodiment may be implemented in
hardware or software, or a combination of both (e.g., programmable
logic arrays or digital signal processors). Unless otherwise
specified, the algorithms included as part of the invention are not
inherently related to any particular computer or other
apparatus.
[0050] In particular, various general purpose machines may be used
with programs written in accordance with the teachings herein, or
it may be more convenient to construct more specialized apparatus
to perform the operations. However, preferably, the embodiment is
implemented in one or more computer programs executing on
programmable systems each comprising at least one processor, at
least one data storage system (including volatile and non-volatile
memory and/or storage elements), at least one input device, and at
least one output device. The program code is executed on the
processors to perform the functions described herein.
[0051] Each such program may be implemented in any desired computer
language (including machine, assembly, high level procedural, or
object oriented programming languages) to communicate with a
computer system. In any case, the language may be a compiled or
interpreted language.
[0052] Each such computer program is preferably stored on a storage
media or device (e.g., ROM, CD-ROM, or magnetic or optical media)
readable by a general or special purpose programmable computer, for
configuring and operating the computer when the storage media or
device is read by the computer to perform the procedures described
herein. The system may also be considered to be implemented as a
computer-readable storage medium, configured with a computer
program, where the storage medium so configured causes a computer
to operate in a specific and predefined manner to perform the
functions described herein.
[0053] The following Example, is provided to illustrate, but not
limit, the scope of the present invention. For example, those
skilled in the art will recognize that the methods and systems of
the present invention are applicable to a wide variety of
differentially responsive sensors, including optical, sound
(resonance), resistance, or other sensors know to those of skill in
the art.
EXAMPLE
[0054] To test the ability of the "electronic nose" to identify
molecules of interest having a particular biological activity
selected from a library of molecules of interest, a quantitative
structure-activity relationship (QSAR) was used to predict the
inhibitory action of a series of alcohols on cytochrome P-450
aniline p-hydroxylation.
[0055] Polymer synthesis and preparation. Polymers were generally
dissolved in tetrahydofuran, except for poly(4-vinylpyridine) and
poly(vinylpyrrolidone), which were dissolved in ethanol, and
poly(ethylene-co-vinyl acetate)(18% vinylacetate),
1,2-poly(butadiene), and poly(butadiene)(36% cis and 55% trans
1-4), which was dissolved in toluene. Each polymer (160 mg) was
dissolved in its respective solvent (20 ml) either at room
temperature or by heating to 35-40 .quadrature. C. for several
hours. Carbon black (40 mg) was added and the suspension sonicated
for at least 20 minutes.
[0056] Sensor Fabrication. Corning microscope slides were cut into
10 mm.times.25 mm pieces to provide substrate for the sensor. A 7-8
mm gap across the middle of each piece was masked while 300 nm of
chromium and then 500 nm of gold was evaporated onto the ends of
the slides to form the electrical contacts. Sensors were formed by
spin-coating polymer/carbon black suspensions onto the prepared
substrates. The resulting films were then allowed to dry
overnight.
[0057] Measurements. An automated flow system consisting of LabVIEW
software, a pentium computer, and electronically controlled
solenoid valves and mass flow controllers were used to produce and
deliver selected concentration of solvent vapors to the detectors.
To obtain the desired analyte concentration, a stream of carrier
gas was passed through a bubbler that had been filled with the
solvent of choice. Saturation of the carrier gas with the solvent
vapor was verified through measurement of the rate of mass loss of
the solvent in the bubbler. The vapor-saturated carrier gas was
then diluted with pure carrier gas through the use of mass flow
controllers (MKS Instruments, Inc). The carrier gas for all
experiments was oil-free air, obtained from the general compressed
air laboratory source, containing 1.10 +/-0.15 parts-per-thousand
(ppth) of water vapor. The air was filtered to remove particulates
but deliberately was not dehumidified or otherwise purified to
reproduce a range of potential "real world" operating environments.
Calibration of the flow system using a flame ionization detector
(model 300 HFID, California Analytical Instruments, Inc.) Indicated
that the delivered analyte concentrations were present.
[0058] Eight bubblers for generation of vapors were available, so
the 22 alcohols and 2 diols were divided into 3 groups of 8 as
indicated in FIG. 5. To pre-condition the sensors, prior to each of
the 3 runs, the sensors were subjected to 40 exposures, 5 to each
of the 8 analytes. Data collection then consisted of a set of 10
exposures to the 8 analytes, with 80 exposures performed in
randomized order to eliminate systematic errors from history
effects. In the third run, bubbler 2 was replaced by a pyrex tube
37 cm in length with a 1 cm inner diameter. This tube was loaded
with approximately 25 cm of granular, solid neopentanol. Flow rates
were calculated to give 100 ml/min of saturated vapor from the
bubblers, which were of sufficient path length to provide saturated
vapors. The background air flow was 1900 ml/min, so that the
analyte concentration delivered to the sensors was 5% of the
analyte's saturated vapor pressure at room temperature. The ability
of the vapor delivery system to provide the expected analyte
concentrations based on the input and control settings to the mass
flow controllers as verified using a calibrated flame ionization
detector that sampled several test analyte gas streams being
delivered to the sensor chamber.
[0059] An exposure had 300 seconds of background air flow, followed
by 300 seconds of flow of analyte at 5% of its saturated vapor
pressure, followed by 300 seconds of the background air. The DC
resistance of each sensor was measured at intervals of
approximately 6 seconds using a multiplexing ohmmeter. The baseline
resistance of a sensor was taken as an average of all measurements
of the resistance of that sensor acquired over a 60 second period
that started between 60 and 66 seconds prior to the start of the
exposure to an analyte. The exact initiation time of this baseline
resistance measurement was different for each sensor, due to small
variations in the time interval required to read the set of 20
resistance values through the multiplexing ohmmeter. The resistance
response for each sensor to an analyte was taken as an average of
all measurements for that sensor in a 60 second period that started
between 234 and 240 seconds after the beginning of the presentation
of the vapor to the sensors, with the exact initiation time for
each sensor channel staggered similarity to that of the baseline
resistance readings. A response was taken to be the change in
resistance of a sensor, .DELTA.R, divided by its baseline
resistance, .DELTA.R. All differential resistance values
(.DELTA.R/R) used in the data analysis represented, or very closely
approximated, the steady-state resistance readings obtained from
the sensors during exposure to the analyte of interest.
[0060] Data Analysis. Initial raw data manipulation and calculation
of responses was performed using Microsoft Excel. Multiple Linear
regression (MLR) was performed using either Excel or the QSAR
{Define} module of the Cerius2 program (Molecular Simulations,
Inc.) on a Silicon Graphics O2 computer. Many possible MLR models
were created, compared, cross-bred, and evolved by the genetic
function approximation on Cerius2.
[0061] Results. FIG. 2 presents the relative differential
resistance responses for various conducting polymer composite
sensors to three representative alcohols, and FIG. 5 summarizes all
of the sensor response data for the various alcohols investigated
in this work. Each alcohol produced a distinct, characteristic
response pattern with the array of sensors chosen for use in the
work. Other sensor arrays comprising different polymer formulatives
are clearly capable of providing response patterns useful in the
present invention.
[0062] The responses of the 19 working sensors to 20 of the
alcohols (FIG. 5) were used to build a QSAR model. Benzyl alcohol
and tert-amyl alcohol were excluded from the fit because their
biological activities were anomalous. The two diols were also
excluded while building the model.
[0063] The inhibitory action data of Cohen and Mannering (Mol.
Pharmacol. 1973, 9, 383-397) are listed in FIG. 5. The values are
expressed as pI.sub.50, where I.sub.50 is the concentration of the
alcohol (in mM) at which the activity of the enzyme is 50%
inhibited, and pI.sub.50 is the negative logarithm of I.sub.50.
More positive numbers correspond to more strongly inhibiting
alcohols.
[0064] The QSAR equations consist of a linear combination of
descriptors whose coefficients are obtained by a least-squares
fitting of predicted to observed biological activity through
multiple linear regression. Equation 1 represents a general set of
QSAR equations, 1 A X 1 , 1 + B X 1 , 2 + C X 1 , 3 + + J X 1 , n +
K = Y 1 (1a) A X 2 , 1 + B X 2 , 2 + C X 2 , 3 + + J X 2 , n + K =
Y 2 (1b) A X m , 1 + B X m , 2 + C X m , 3 + + J X m , n + K = Y m
(1m)
[0065] where Y.sub.1 is the biological activity of the i.sup.th
Molecule, X.sub.i,j is the value of the j.sup.th descriptor for the
ith molecule, and A, B, C, . . . K are constants that are obtained
through the fitting of Y.sub.1, (predicted) versus Y.sub.1
(observed). In Equation 1, the i.sup.th alcohol's inhibitory
activity is represented by Y.sub.1 and its n sensor responses are
taken as its descriptors (X.sub.i,l to X.sub.i,n)
[0066] The genetic function algorithm of the QSAR module of Cerius2
was used to select the best sensors for the QSAR. One hundred
multiple linear regression models were generated from random
combinations of 4 sensors. These models were ranked according to a
lack-of-fit (LOF) parameter, as given by equation 2: 2 LOF = LSE (
1 - ( ( c + dp ) / m ) ) 2 ( 2 )
[0067] LSE is the least-squares error, c and p are both the number
of descriptors (sets of relative differential resistance response
of the sensors in the array) for a simple linear model such as the
one herein, M is the number of samples (e.g., alcohols), and d is
the "smoothing parameter", which is entered by the user (1.0 was
used). The LOF value is therefore an inverse measure of how well
the model fits the data, with a penalty for the use of a large
number of descriptors relative to samples. From the set of 100
models, two "parents" are chosen, with a probability inversely
proportional to their LOF, and "crossed over"--some of the
descriptors from each are used to form a new model. There is then a
probability for "mutation", where a new, randomly chosen,
descriptor is added to the "daughter". If the daughter is not
already present in the population, it replaces the model with the
worst LOF from the population. After 5,000 rounds of genetic
operation, convergence is generally reached, in which the optimal
models have been found.
[0068] When the 19 sets of responses from the working sensors were
given to the Genetic Function Algorithm (GFA), a model that
incorporated 5 of the sensors was found to be optimal. The best fit
is described by equation 3: 3 pI 50 = 0.51 - 3 + 1.90 - 9 - 3.58 -
13 - 2.14 - 15 - 0.90 - 18 - 1.29 n = 20 R = 0.995 s = 0.092 F =
297
[0069] The numbers in bold refer to sets of responses from the
sensors with those numbers, n is the number of samples, R is the
correlation coefficient, and s is the standard error. The
correlation coefficient of 0.995 indicates that the fit was quite
good. The F statistic of 297 indicates that the overall
significance of the fit is very high, in fact is at a level of
1-10.sup.-13. Coefficients for all sensors are significant far
beyond the 99.9% level, as attested to by their t statistics (see
table 3). Predicted versus experimental pI.sub.50 values are
plotted in FIG. 3.
3TABLE 3 Regression Statistics For the Coefficients of Equation 3
Coefficient Standard Error t Stat P-value Intercept -1.29 0.27
-4.71 3.32E-04 3 0.51 0.07 6.93 6.98E-06 9 1.90 0.19 9.92 1.03E-07
13 -3.58 0.21 -17.13 8.70E-11 15 -2.14 0.27 -7.91 1.56E-06 18 -0.90
0.08 -11.34 1.94E-08 The t statistic is equal to the value of the
coefficient divided by its standard error; it is used to derive the
P value, which indicates the significance of the coefficient.
[0070] Methanol has an inhibition activity distinctly different
from that of the other alcohols, and this can lead to a
misleadingly good fit through a "point and cluster" effect. A
second least-squares fitting of equation 3 was performed with the
exclusion of methanol. The coefficient of 15 changed from -2.14 to
-2.20, while those of the other sensors remained nearly the same.
The overall quality of the fit declined; F decreased from 297 to
109, corresponding to a decrease in the significance of the fit
from the level of 1-(1.times.10.sup.-13) to 1-(4.times.10.sup.-10).
The decrease quality of the fit occurs because methanol is modeled
well by the equation, but when methanol is excluded there is much
less variation in the data to be fit.
[0071] Electronic Nose-Based QSAR. The selection of which molecules
to include in a QSAR is crucial. In the sense, that it is desirable
to use the broadest set of molecules available to build a QSAR,
while not including only one or two molecules from a distinctly
different class of compounds. For example, benzyl alcohol, the only
aromatic alcohol in the data set, has a higher activity than is
predicted by both our QSAR and another QSAR on the cytochrome P-450
system. The anomalous activity of benzyl alcohol could be accounted
for with an additional descriptor unique to benzyl alcohol, but the
choice of such a parameter is rather arbitrary, so benzyl alcohol
was excluded during the building of our QSAR. Tert-amyl alcohol was
also excluded because there is evidence that tertiary alcohols
function through a stimulatory mechanism in addition to the usual
inhibitory mechanism. As would be expected in tert-amyl alcohol
were also acting through this stimulatory mechanism, its inhibitory
activity is anomalously low. The two diols were also excluded while
building the model. Because of these limitations, the QSAR is
expected to be most successful at predicting the activity of
aliphatic mono-alcohols having no other functionalities.
[0072] The sensors chosen for the model by the GFA are among those
whose responses are most reproducible. Reproducibility was measured
by examining the set of 10 response of a given sensor to a given
analyte. The value S.sub.i,j is defined as the standard deviation
among the 10 responses of the j.sup.th sensor to the i.sup.th
alcohol divided by the average of those responses. Each sensor has
a set of 20 S values, one for each alcohol. A sensor's
reproducibility can be gauged by the median of its set of S values.
Four of the five sensors used in the model displayed median S
values less than 0.063, raking them among the best seven sensors.
The only sensor outside this group, 15, responded only to very
polar analytes. Since its response to the majority of the analytes
was quite small, its S value for those analytes is very large.
However, for the analytes to which it did respond, for example
methanol and ethanol, its S values are small, 0.040 and 0.041,
respectively. The inclusion of 15 might be questioned if it were
necessary only to model the activity of one analyte, namely the
outlier methanol. To test the validity of including 15 in the QSAR,
equation 3 was refit with the same set of sensors and all of the
previously used alcohols, excluding methanol. In the new QSAR, the
significance of 15 remains significant. If the set of five sensor
responses to methanol are substituted into the second QSAR
equation, which was formed with no information about methanol, the
predicted pI.sub.50 of methanol is -3.12 very close to its
experimental value of -3.09. It appears that whatever molecular
characteristics are probed by 15 are successfully extrapolated from
the more moderately polar analytes to methanol. In other words, 15
is not just an indicator variable for methanol that is fit with an
arbitrary coefficient.
[0073] A quantitative measure of the predictive power of the QSAR
can be obtained by building a model using the biological and sensor
response data from all the molecules except one, and then
predicting the activity of the excluded molecule with that model.
The procedure is repeated for each molecule in the data set, and
the predictive sum of squares (PRESS) is defined as the sum, over
all analytes, of the squared differences between the predicted and
actual biological activity. Using equation 3, the PRESS for the set
of 20 alcohols is 0.221. This value can be compared to the residual
sum of squares, RSS, in which one QSAR equation (fit to all
samples) is used to calculate the predicted activity. As would be
expected, the RSS of 0.117 is lower than the PRESS. More
significantly, a large difference between the PRESS and RSS would
imply that the model had used too many parameters and overfit the
data, and this appears not to be the case.
[0074] An optimum fit (as judged by the LOF parameter) was found to
require five descriptors; no equation with a different number of
descriptors formed as significant a model. The best 4 sensors QSAR,
consisting of sensors 1, 13, 16 and 17, has an R=0.984, s=0.163,
and F=114, indicating an overall significance at the level of
1-(5.times.10.sup.-11). On the other hand, addition of further
sensors adds parameters and enables a better fit to the data set.
However, if 4 is added to equation 3 to form the best 6-sensor
equation, certain key statistics point to a diminished model. As
would be expected with an additional parameter, R increases, from
0.995 to 0.996. Additionally, the standard error decreases from
0.0916 to 0.0834, the RSS decreases from 0.117 to 0.090, and the F
statistic increases from 297 to 300. However, the significance of
the fit, represented by the F statistic, decreases from
1-(1.08.times.10.sup.-13) to 1-(3.66.times.10.sup.-13). The PRESS
increases from 0.221 to 0.253. Thus, although the 6-sensor model
fits the set of 20 alcohols better than the 5-sensor model, the
6-sensor model is worse at predicting the activity of an alcohol
that was not included in the fit, indicating that the 6-sensor
model has overfit the data.
[0075] As described above, the cytochrome P-450 p-hydroxylation
inhibition activities of all the aliphatic mono-alcohols
investigated in this work could be quite accurately predicted from
a model that was constructed without the use of any information
about the molecular structure of the alcohols for which the
prediction are made. This indicates that the resistance data output
of the electronic nose contains implicit information on most of the
chemical factors that control the interactions of the enzyme with
the alcohols. These resistance data reflect the binding
interactions between the alcohols and a collection of polymers
having a diverse collection of chemical attributes. It is not
necessary that an individual polymer probe specifically and
exclusively one such descriptor of the analyte-substrate
interaction, because the desired information can be obtained
through analysis of the collective response of the sensor array to
an analyte.
[0076] Comparison with Other QSARs. Cohen and Mannering fit the
activity of 11 of the unbranched 1 - and 2-alcohols (excluding
methanol) to a one parameter equation using log P (J. Mol.
Pharmaco. 1973, 9, 383-397). A modified version, using updated log
P values and fit to only 10 alcohols (excluding methanol and
ethanol), was given later by Shusterman (equation 4) (Chem.-Biol.
Interactions 1990, 74, 63-77). 4 pI50 = 0.43 log P - 0.53 N = 10 R
= 0.954 s = 0.128 ( 4 )
[0077] However, Shusterman also showed that for a larger set of
alcohols, a simple fit to log P was inadequate to describe most of
their activity; a fit of 19 alcohols yielded equation 5, which has
rather poor regression statistics. 5 pI50 = 0.35 log P - 0.71 n =
19 R = 0.505 s = 0.468 ( 5 )
[0078] In a second equation using two descriptors, log P and (log
p) 2, Cohen and Mannering fit 17 of the alcohols with an R of 0.98
(equation 6). 6 pI 50 = 1.50 log P - 0.36 ( log P ) 2 + 1.75 n = 17
R = 0.98 s = 0.44
[0079] Although this was a better fit, it used more descriptors.
Additionally, it is evident from inspection of the data that there
are factors besides hydrophobicity that determine an alcohol's
activity. Four subsequent QSARs have therefore been used to model;
the data set more fully and some aspects of these models are
discussed below.
[0080] A more complex, three parameter, QSAR was based upon logP, a
calculated electronic parameter (.epsilon..sub.HOMO), and a steric
parameter (BULK.sub.lat)(equation 7). 7 pI 50 = 16.2 log P - 16.0
log ( P + 1 ) - 1.35 BULK lat + 0.381 HOMO + 22.5 n = 21 R = 0.982
s = 0.170 log = 1.05 ( 7 )
[0081] Shusterman and Johnson, however, pointed out that the use of
.epsilon..sub.HOMO as a parameter was unjustified since it was
necessary only to fit benzyl alcohol, and becomes an insignificant
parameter (as indicated by its t value) when benzyl alcohol is
excluded from the data set. Similarly, the bilinear dependence of
pI50 upon log P of equation 7 was necessary only to fit a single
data point, methanol.
[0082] Another QSAR, based on a choice of molecular connectivity
indices, has also been used to model the activity of 20 alcohols
(benzyl alcohol and tert-amyl alcohol were excluded (equation 8). 8
pI50 = - 6.88 ( 1 / o v ) - 1.14 4 PC + 1.85 n = 20 R = 0.983 s =
0.156 ( 8 )
[0083] The parameter .sup.o.chi..sup.v, the zero-order valence
molecular connectivity index, basically corresponds to molecular
size, and therefore hydrophobicity, for this set of molecules.
Hence, the inverse of the index has a negative coefficient in
equation 8. The parameter .sup.4.chi..sub.PC, the fourth-order
path/cluster molecular connectivity index, correlates with the
degree of branching in the molecule, and therefore also has a
negative coefficient in equation 8.
[0084] A third QSAR, which relies entirely upon calculated
electronic parameters as descriptors, has been constructed and used
to fit all 22 alcohols. Shusterman noted problems with the QSAR.
For example, it was asserted that the .alpha.-carbon of the
alcohols was acting as an electron acceptor from the enzyme,
because a correlation between activity and QCL, the electron
density on the .alpha.-carbon in the LUMO, was found. QCL is
correlated with log P(R=0.747), to some extent explaining the fit.
Two alcohols, 3-methylbutanol and 2,4-dimethyl-3-pentanol, were
poorly fit, and no rationalization was presented for why the
correlation with QCL would not apply to these two substrates as
well.
[0085] Finally, Shusterman created a QSAR based on log P and two
steric parameters, M and A, which were used to describe the
branching of the alcohols. M is the number of carbons beyond the
methyl substituent in FIG. 3, thus, 1- and 2-alcohols have an M=0,
while M for 3-pentanol would be one, and for
2,4-dimethyl-3-pentanol is 2. The second parameter, A, refers to
the number of branched carbons in the main chain, A=1 for
2-methyl-1-butanol and 2 for neopenyl alcohol. A fit of 19 of the
alcohols (benzyl alcohol, tert-amyl alcohol, and methanol were
excluded) yielded equation 9. The negative coefficient for M and A
indicate the loss of activity with branching. 9 pI50 = 0.48 log P -
0.65 .cndot. M - 0.31 .cndot. A - 0.60 n = 19 R = 0.955 s = 0.171 (
9 )
[0086] To compare the electronic nose QSAR to those of Sabljic and
Shusterman, one must use statistics that take into account the
number of descriptors used. Table 4 lists the comparison of
selected regression statistics from the QSAR of Sabljic,
Shusterman, equation 3, and the QSAR created when the coefficients
of equation 3 were fit to the 19 alcohols besides methanol (R is
the correlation coefficient, s is the standard error, and the final
column is the overall significance of the regression equation).
Because the electronic nose QSAR model uses more parameters, it is
inappropriate to compare just either the correlation coefficients,
standard error, or residual sum of squares of the models. To some
extent, the PRESS should be independent of the number of parameters
in a model, since the model is tested upon molecules about which it
has no information. The PRESS of the electronic nose QSAR model is
significantly lower than the other two models of interest. Finally,
the F steatitic gauges the overall significance of the fit while
accounting for the number of parameters used. By this measure, the
electronic nose QSAR is approximately as significant as Sabljic's
and more significant than Shusterman's.
4 TABLE 4 Data descriptors pts fit used R s RSS PRESS F
Significance F Sabljic 20 2 0.983 0.156 0.414 0.872 250 2.51E-13
Shusterman 19 3 0.956 0.17 0.436 0.786 53 3.34E-08 Present 20 5
0.995 0.092 0.117 0.221 297 1.08E-13 Disclosure Present 19 5 0.988
0.095 0.117 0.243 109 3.89E-10 Disclosure (no methanol)
[0087] It appears that the important chemical interaction involved
in the partitioning of the aliphatic alcohols into the enzyme
binding site are probed by the array responses. The construction of
our QSAR did not require making assumption regarding which steric
or electronic factors are important or what parameters to use to
capture such effects. Obtaining chemical insight into the nature of
the dominant binding forces involved in the reaction being modeled
would require a complete understanding of the chemical factors that
determine the analyte partitioning into each polymer in the
electronic nose. In principle it is possible to extract such
information for certain descriptors of interest, but it is not
necessary to have such information in order to use the
readily-obtained electronic nose data to predict successfully the
activity of various alcohols in inhibiting cytochrome P-450
activity.
[0088] Although only a few embodiments have been described in
detail above, those having ordinary skill in the art will certainly
understand that many modifications are possible in the preferred
embodiment without departing from the teachings thereof.
[0089] All such modifications are intended to be encompassed within
the following claims.
* * * * *