Compounds {drug, chemistry} {drug-like compound} can metabolize with biological molecule.
size
Drugs have molecular weight 200 to 700.
side effects
Drugs must have few side effects.
absorption
Body can absorb drugs.
distribution
Drugs can go to body organs and/or tissues.
metabolism
Drugs have chemical reactions at sites. Drugs have orientation at receptor site. Drugs can sterically interact with receptor site.
excretion
Drugs do not excrete too quickly.
solubility
Drugs have solubility, partition coefficients, diffusivity, and ionization degree.
variation
Drugs can vary using different salts, esters, and side groups for different sizes and surface areas.
form
Drugs can be solutions, suspensions, capsules, or tablets. They can be oral, subcutaneous, intravenous, inhaled, or patch.
history
In England, William Morton [? to 1868] used inhaled ether [1846] during surgery on October 16 (Ether Day). inhaled chloroform [1850]. inhaled nitrous oxide and oxygen [1868]. hypodermic syringe [1868]. intravenous morphine [1868]. chloral hydrate [1869]. inhaled nitrous oxide and oxygen followed by chloroform or ether [1876]. paraldehyde [1882]. cocaine [1884]. sulfones [1888]. ethyl p-aminobenzoate [1890]. Novocaine is procaine hydrochloride. Phenacetin comes from aniline by hydroxylation and conjugation [1890 to 1899]. aspirin [1899]. Anti-pyrine came from quinine [1900]. urethane [1900].
Organizations regulated by the Food and Drug Administration (FDA) are required to comply with Good Laboratory Practices {good laboratory practices} (GLP). GLP compliance requires organizations to have administrative policies, written procedures, competent personnel, and trained personnel. As part of GLP compliance, software products used in regulated organizations should comply with FDA regulations and document how compliance was achieved.
Code of Federal Regulations (CFR), Title 21, Chapter I, Part 11
Specific functions, electronic records, and auditing of software systems are required to be compliant with Code of Federal Regulations (CFR), Title 21, Chapter I, Part 11, Electronic Records; Electronic Signatures Final Rule (FDA CFR21 Part 11).
FDA CFR21 Part 11 requires accurate, reliable, and consistent software.
FDA CFR21 Part 11 does not necessarily require encryption.
FDA CFR21 Part 11 requires versioning of data and audit records.
FDA CFR21 Part 11 requires data to be entered in specific fields before processing.
FDA CFR21 Part 11 requires auditing.
FDA CFR21 Part 11 requires electronic signatures.
FDA CFR21 Part 11 has installation requirements. All necessary software components must be successfully installed and a report generated.
FDA CFR21 Part 11 has logon and logoff requirements. Systems limit access to only authorized persons, by checking user name and password. After a specific time period, automatic logoff occurs.
FDA CFR21 Part 11 has security requirements for data and audit record management, with file and operating system permissions. Attempts at unauthorized use are sent by electronic mail to the Administrator. User and user groups have privileges to files, directories, and functions. Systems can detect invalid or altered records. Auditing of user events detects creation, modification, and deletion of files, using checksums.
FDA CFR21 Part 11 requires instrument maintenance logs.
FDA CFR21 Part 11 has requirements for reporting data, parameters, and auditing information.
Drugs have absorption, distribution, metabolism, and excretion {ADME}.
Pharmacokinetics (PK) is about absorption, distribution, metabolism, and excretion {ADME/PK profile}.
Drug Metabolism and PharmacoKinetics {DMPK}.
Inactive chemicals {excipient}, such as solvent or powder, can carry active drugs.
Plasma proteins {human serum albumin} (HSA) can carry other molecules.
Drugs {pharmacodynamic drug, complex} can make complexes but not cause chemical reactions or conformational changes.
Drugs have absorption, distribution, metabolism, and elimination {pharmacodynamics} (PD).
Population genotypes can identify SNPs affecting drug metabolism {pharmacogenomics}.
Absorption, distribution, metabolism, and elimination affect drugs {pharmacokinetics} (PK).
High-enough concentration {potency}| causes biologic response.
Drugs {prodrug} can require metabolization to transport or be active.
Drugs can cause birth defects {teratogenicity}|, by acting on development processes.
Drug can damage tissues {toxicity}|.
Foreign compounds {xenobiotics} are vapors, alcohol, drugs, pollutants, solvents, food toxins, pesticides, and pyrolysis products. Pyrolysis products come from charring fat or protein.
Drugs have activity {drug, activity}, depending on structures and other factors.
Activity is half maximum at a concentration {IC50}.
Mopac quantum-mechanical calculation can find activation energy {initial activation energy} (Ea0).
Structure can associate with physicochemical property {property-activity relationship}.
Measured activity equals physicochemical-variable function {quantitative structure-activity relationship} (QSAR). QSAR relates activity magnitude, such as tissue concentration, to compound physico-chemical or structural property magnitudes, such as carbon-atom numbers. QSAR (3D-QSAR) can be in three dimensions.
Activity equals physicochemical-variable function {structure-activity relationship} (SAR).
Structures and properties have relation {structure-property correlation} (SPC).
Systems {Corey Pauling Koltun} (CPK) can display space-filling compound models.
Molecule alignments can adjust {field-fit procedure}.
Indexes {kappa index, drug} can depend on molecular shape and flexibility.
Network mappings {Kohonen topology-preserving mapping} can retain topology.
Calculations {Morgan algorithm} can make unique numberings for connection tables.
Strings {SMILES} can uniquely describe three-dimensional structure.
Searches {substructure searching} can use connectivity-table parts as search criteria.
Topological indexes {Tanimoto index} can represent graphs as numbers.
Indexes {topological index} can represent graphs as numbers.
Indexes {valence molecular-connectivity index} can use valence to indicate connectivity.
Sums {branching index} over all bonds, of inverse of square root of end-atom-valence product, can measure branching amount.
Indexes {molecular connectivity index} can depend on branching.
Normal-distribution outlier tests {Dixon's Q-test, drug} {Dixon Q-test, drug} can measure smallest and largest difference ratio.
Normal-distribution outlier tests {Grubbs' s-test, drug} {Grubbs s-test, drug} can compare absolute value, of difference between mean and value, divided by standard deviation, to T-distribution value.
Rules {Active Analog Approach} can align molecule activities by analogous structures.
Rules can align molecule activities by structural group {active pharmaceutical ingredient} (API).
Non-parametric methods {alternating conditional expectations} (ACE) can analyze activity.
Input "neuron" layer can hold physico-chemical properties and feed to middle layer using sigmoidal function {transfer function} with weights for outputs. Middle-layer "neurons" feed to one output {artificial neural network} (ANN).
Mathematical tools {chemometrics} applied to structure-activity relationships can find correlations and regression, recognize patterns, classify compounds and properties, design experiments for random screening and measuring, and validate results.
Quantum mechanics can pair with empirical approaches {computer-assisted metabolism prediction} (CAMP).
Cell arrays can pool more than one sample in cells, which allows fewer cells. Methods {deconvolution} can track sample pooling.
convolution
Convolution puts each sample into several cells, in regular pattern. Testing looks for one effect. Some cells show effect, but most do not. If sample causes effect, all cells with that sample show effect. Cells that contain that sample form pattern, so pattern indicates sample name.
deconvolution
Deconvolution uses convolution method and resulting cell pattern to find sample name. For example, for 100-cell array, 10 samples can feed into 90 cells, each cell receiving two samples. Ten cells have control samples. See Figure 1. Samples are in 18 cells. If testing shows that all 18 have activity over threshold, then that sample is effective.
If sample interactions cause effect, deconvolution can find interactions. If testing shows that only one cell has activity over threshold, those two samples must interact to be effective.
Combining quantum mechanics and physico-chemical properties {empirical-quantum chemical} {combined empirical/quantum chemical approach} can predict chemical behavior.
Models {Korzekwa-Jones model} can be for P-450 hydrogen abstraction and depend on difference between radical free energy and hydrogenated-atom free energy, as well as radical ionization potential and constant additive term.
Steric effects and van der Waals forces can cause fields {Lennard-Jones potential}.
Plots {loading plot} can use variable weights.
Semiempiric methods {modified neglect of differential overlap} (MNDO) can ignore overlap.
Molecule-modeling programs {molecular modeling}, such as Alchemy III and SYBYL from Tripos, can use electrostatics or quantum mechanics.
Non-parametric methods {non-linear partial least-squares, drug} (NPLS) can find least squares.
Response-surface methods {non-parametric method}, such as ACE, NPLS, and MARS, can be non-parametric.
IF/THEN statement sets {rule induction system, drug} can make output from input.
Graphs {score plot} can plot compound activities.
In multidimensional property space, compound clusters make classes separated by distance {cluster analysis} (CA). CA reduces unimportant variables. Substructure, topological index, physico-chemical property, calculated physico-chemical property, or hydrophobicity can determine classes.
Using discrete or continuous data and embedded data can put compounds into groups by activity level {cluster significance analysis} (CSA). CSA locates small clusters in large spaces.
Methods {Cone and Hodgkin similarity index} can measure molecular similarity.
Models {discriminant-regression model} (DIREM) can locate small clusters in large spaces.
Methods {distance-b program} (EVE) can locate small clusters in large spaces.
Unsupervised methods {hierarchical cluster analysis} (HCA) can measure distances between all points and make point vs. distance dendograms.
Structures can cluster in large databases by rating different compounds by similarity {Jarvis-Patrick method}.
Supervised methods {k-nearest neighbor} (k-NN) can calculate new-object distances from all other objects, to locate small clusters in large spaces.
Processes {partitioning} can merge individuals into groups or split whole into clusters.
Values {similarity measure} can compare distances.
Methods {single class discrimination} (SCD) can locate small clusters in large spaces.
Classifications {supervised method} can use already known patterns and clusters.
Activity and descriptor correlation vectors {trend vector analysis} can rank compound similarity.
Hierarchical methods {Ward's clustering method} {Ward clustering method} can agglomerate compounds to find clustering.
Supervised methods {Soft Independent Modeling of Class Analogies} (SIMCA) can use region-boundary or envelope models, to locate small clusters in large spaces.
Clustering methods {class analogy} can be SIMCA methods.
Distance measures {city-block distance} between structure-space points can be the same as Manhattan distance.
Distance measures {Manhattan distance} between structure-space points can be the same as city-block distance.
Distance measures {Minkowski distance} between structure-space points can be the same as Lp-metric.
Distance measures {Lp-metric} between structure-space points can be the same as Minkowski distance.
Structure-space points have distances {Mahalanobis distance}.
Hierarchical methods {centroid linkage} that agglomerate compounds can find clustering.
Hierarchical methods {complete linkage} that agglomerate compounds can find clustering.
Hierarchical methods {single linkage} that agglomerate compounds can find clustering.
Processes have factors {factor analysis}. Physico-chemical or structural properties describe compounds and have components {descriptor, factor} {X-variable, factor} {X descriptor, factor}. Chemical activities relate to variables {response variable}.
Methods {canonical factor analysis} can be for factor analysis.
Methods {centroid method} can be for factor analysis.
QSAR {combinatoric QSAR} can find similarities using different descriptor combinations.
Moments of inertia, and dipole and quadrupole moments, can be descriptors to calculate molecular moments {Comparative Molecular Moment Analysis} (CoMMA). CoMMA depends on shapes and charges.
Properties and structures have relations {Correlation Analysis}.
Factor-analysis methods {correspondence analysis} {correspondence factor analysis} (CFA) can use variable frequencies relative to activities, finds chi-square values, and finds principal components.
Principal components {disjoint principal component} (DPP) can be independent.
Thresholds {eigenvalue-one criterion} can be how many components have eigenvalues greater than one.
Unsupervised linear methods {eigenvector projection} can find factors.
Models {Evolutionary Programming} (EP) can add and subtract randomly selected variables, with crossing-over, and evaluate for "fitness" or best fit.
Methods {evolving factor analysis} (EVA) can analyze ordered data.
Methods {percentage of explained variance} {explained variance percentage} can indicate number of components required to reach 90% of total variance.
Parameters and descriptors can linearly relate to free energy {extrathermodynamic approach}.
Factor-analysis methods {free energy perturbation} (FEP) can use free-energy changes.
Binary descriptors can note molecule-substructure presence or absence {Free-Wilson approach}.
Linear property sets can have different values, change values by crossing-over between related such genes, and have random change {Genetic Function Algorithm} (GFA), to select best fit.
Values {Hammett sigma value} can relate to electronic and electrostatic properties.
Activity, partition coefficients for hydrophobicity, ionization degree, and molecular size relate {Hansch equation}.
Variables {latent variable} can be linear-descriptor combination.
Supervised methods {linear discriminant analysis} (LDA), in which boundary surface minimizes region variance and maximizes variance between regions, can put compounds into groups by activity level.
log K = k1 * sigma + k2 {linear free energy equation, drug} (LFE).
Supervised methods {linear learning machine} (LLM) can divide n-dimensional space into regions, using discriminant function.
Factor-analysis methods {maximum-likelihood method} can find factors.
Metric or non-metric methods {multidimensional scaling} (MDS) can analyze similarity or dissimilarity matrices to find dimension number and place objects in proper relative positions.
Non-parametric methods {multivariate adaptive regression spline} (MARS) can find factors.
Models {Mutation and Selection Uncover Models} (MUSEUM) can add and subtract randomly selected variables, with no crossing-over, and evaluate for "fitness" or best fit.
Unsupervised linear methods {non-linear iterative partial least-squares} (NIPALS) can represent data as product of score matrix, for original observations, and loading-matrix transform, for original factors.
Topological mappings {non-linear mapping} (NLM) can be factor-analysis methods in which linear-variable combinations make two or three new variables.
Information about compound physico-chemical properties can predict compound chemical or physiological behavior in vitro and in vivo {predictive computational model}.
Variables {principal component} (PC) can be linear-descriptor combinations. Unsupervised linear method {principal component analysis, factor} (PCA) represents data as product of score matrix, for original observations, and loading-matrix transform, for original factors. PCA is factor-analysis method in which linear variable combinations make two or three new variables. PCA reduces unimportant variables.
Singular-value decomposition (SVD) can find best singular values for predicting {principal component regression} (PCR). SVD projects regression to latent structures.
Modified PCA {principal factor analysis} can find principal factors.
Methods {Procrustes analysis} can identify descriptor sets for describing similarity.
Methods {QR algorithm} can diagonalize matrices.
Unsupervised linear methods {rank annihilation} can find factors.
Residual variance approaches constancy {Scree-test, drug}, and plotted slope levels off {Scree-plot}, depending on component number.
In unsupervised linear methods {singular value decomposition, drug} (SVD), correlation matrix is product of score, eigenvalue, and loading matrices, with diagonalization using QR algorithm.
Factor-analysis methods {spectral mapping analysis} (SMA) can first take data logarithm to eliminate outliers and then subtract means from rows and columns, to leave only variation, showing which variables are important and how much.
Spaces {structure space} can have two or three principal components.
Methods {target-transformation factor analysis} can rotate features to match known pattern, such as hypothesis or signature.
Factors and response variable have relations {Unsupervised Method}, without using factor information or predetermined models.
Designs {factorial design} can try to ensure design-space sampling, if position varies.
Designs {fractional factorial design} can try to ensure design-space sampling, if position varies.
Three-level designs {response surface method} (RSM) can have three factors that quantify relationships among responses and factors. RSM includes MLR, OLS, PCR, and PLS linear designs; non-linear regression analysis (NLR); and non-parametric methods, such as ACE, NPLS, and MARS.
isomer-enumeration method {Cayley tree structure}.
Isomer-enumeration methods {CONGEN program} can be successors to DENDRAL.
Isomer-enumeration methods {DENDRAL program} can be forerunners of CONGEN.
isomer-enumeration method {Henze and Blair recursion formulas}.
Isomer-enumeration methods {Polya's enumeration theorem} {Polya enumeration theorem} can use group theory.
Electron orbitals {molecular orbital} can be for whole molecule.
Analyses {ab initio analysis} can use all electrons.
Adding atomic orbitals can approximate molecular orbitals {linear combinations of atomic orbitals} (LCAO).
Semiempiric methods {perturbative configuration interaction using localized orbitals} (PCILO) can use perturbations.
Analyses {semiempiric} can use valence electrons and parameterize core electrons.
Sigma electrons can contribute {simple delta index, drug}.
Factors, properties, or structures {regressor} can contribute to response values {regression, regressor} {Regression Analysis}.
Regression can project to latent structures {canonical correlation} (CC), to put compounds in classes.
Regression {continuum regression} (CR) can project to latent structures, to put compounds in classes.
Variance-covariance matrix {correlation matrix, drug} can scale to normalize data.
Regression can project to latent structures {kernel algorithm}, to put compounds in classes.
Methods {matrix diagonalization, drug} can simplify data variance-covariance matrix.
Parametric methods {non-linear regression} (NLR) can find descriptor coefficients by non-linear regression.
Regression can project to latent structures {ridge regression} (RR), to put compounds in classes.
Methods {Spearman rank correlation coefficient} can measure molecular similarity.
Complete, symmetric, square matrix {variance-covariance matrix} uses property values and structure values.
Regression can project to latent structures {adaptive least-squares} {ALS algorithm}, to put compounds in classes.
Methods {classical least-squares, drug} (CLS) can be the same as ordinary least-squares analysis.
Partial least-squares {Comparative Molecular Field Analysis} (CoMFA) can analyze grid around site atom and find grid-point electrostatic and steric interactions, to make sampled-point descriptors.
Compounds have different classes with different weights {fuzzy adaptive least-squares} (FALS).
Methods {Generating Optimal Linear PLS Estimations} (GOLPE) can use PLS and D-optimal design to select variables, and cross-validates.
Fitting methods {inverse least-squares} (ILS) can find regression line.
Methods {least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {linear least-squares regression, drug} can be the same as ordinary least-squares analysis.
Partial least-squares methods {matrix bidiagonalization method, drug} can simplify data variance-covariance matrix.
Regression can project to latent structures {multi-block PLS}, to put compounds in classes.
Methods {multiple least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {multiple linear regression} (MLR) can measure linear component dependence on physico-chemical or structural properties and finds descriptor coefficients.
Methods {multivariate least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {non-least-squares} (NLS) can detect non-linear relationships.
Fitting methods {ordinary least-squares} (OLS) can find descriptor coefficients.
Methods {partial least-squares} (PLS) can use least-squares to find independent variables and dependencies among variables. It projects regression to latent structures. It maximizes latent-variable and observable covariation. It diagonalizes the matrix.
Methods {SAMPLS algorithm} can apply PLS to trend vector analysis.
Estimates {best linear unbiased estimator} (BLUE) can give smallest variance among estimators.
Error measures {standard error} can be square root of MSE.
SSE, SSR, or SST {sum of squares of differences} {squares of differences sum}.
SSE / (observation number + factor number - 1) {mean square error} (MSE).
Errors or residuals can cause sum {SSE} of squares of differences between observed and predicted responses.
Regression can cause sum {SSR} of squares of differences between observed and mean.
Sum {SST} of squares of differences between predicted and mean makes total: SST = SSE + SSR.
Drugs have tests {drug experiment}.
experimental design
Samples can test properties and activities. Experiment uses numbers and sample types from population, as well as methods and instruments. Three-level design assigns three levels (-1,0,1) to each factor to determine how responses vary with factors or variables, for making mechanistic physico-chemical models, using physical chemical properties as factors, or empirical polynomial models, using arbitrary variables as factors. Three-level mixture design determines whether factor is useful or significant or not. Two-level design assigns two levels (0,1) to each factor to determine whether factor is useful or significant, for screening, searching, or filtering.
kinetics
Experiments can read samples multiple times over time to find reaction rate or inhibition constant.
Biological reaction series can makes protocols {assay, experiment}. Protocol or method series use reagents to identify compounds, genes, proteins, or quantities.
Methods {protocol, experiment}| can run experiments. Experiments can perform steps or tasks on samples: prepare samples, mix with reagents, hybridize, wash, detect, and analyze.
Samples can be read more than once {replication, sample}.
In screening, calculated results translate into given ranges {scoring}, like high, medium, or low.
three-level design {Box-Behnken design}.
Experimental designs {CARSO approach} can be for random compound screening in experiment series.
three-level design {central composite design}.
Experimental designs {Craig plot} can be for random compound screening in experiment series.
Test-set selection methods {D-optimal design} can try to ensure design-space sampling, if positions vary, and can account for excluded volumes.
Experiment designs {sequential optimization} can use steps toward optimum.
Experimental designs {Topliss tree} can be for random compound screening in experiment series.
Experiments {dose response curve} can read samples at different concentrations and fit IC50 curves.
Experiments {ELISA} can back-calculate sample concentration, using reference curve.
Automated assays {High-Throughput Screening} (HTS) can test many diverse compounds against enzymes or cell targets, to identify possible new drugs.
More than one sample can be in wells {pooling} in screening experiment. Samples mix in plate wells according to patterns, so system measures all samples the same number of times. Total well number is fewer than with one sample per well.
Experiments {ratio experiment} can read samples twice, for agonist vs. antagonist, to determine activity ratios.
Automated assays {screening} {high-throughput screening} can identify promising compounds from compound libraries.
users and groups
Roles (types of users) have a set of privileges. Users have roles.
inventory
Inventory database has sample IDs.
experiment type: ELISA
Plate has high and low controls (averages), dose-response titration (IC50), and replicated samples (averages of % inhibition); Fit the result into the standard curve for a first estimate IC50, and back calculate the concentration of an expressed protein by using a standard reference curve on each plate. Look for the well that has a result which falls on the linear portion of the standard curve.
experiment type: ratio
Each plate is read twice, first reading the growth of a specific protein, and second measuring the survival of various cells (or agonist vs. antagonist receptors). % inhibitions are calculated for each of these readings, and then the ratio. Sort on any of the three results. Since plates are carried from one reader station to the next, they may get out of order, may be put into the reader backwards, or may get dropped, so data system should be able to deal with all of these problems. This is a case of 'multiple data points per well'.
experiment type: dose response, multiple calculations
Several different models calculate, fit, and display the IC50 curves simultaneously, like straight line regression and 4-parameter curve fitting, with constant high and low inflection points. Then register one or more, with model name, parameters, and comments.
experiment type: titration
From each daughter plate, 3 to 4 assay plates at different concentrations are made. An activity value is calculated for each well, as well as the activity changes as a function of concentration.
experiment type: titration and dose response across plates
A single 96 well plate is filled with 48 samples in duplicate. The plate is then copied across 8 other plates at different concentrations. The result from the 9 plates is used to calculate a dose response curve and IC50.
experiment type: no controls on data plates
All controls (high, low, reference, dose-response curve) are on one or more reference plates, interspersed throughout the runset. Calculate a 'curve' for the standards on the reference plates, by plate sequence or time. Could use controls only on nearest reference plate.
experiment type: whole plant experiment
Growth of different plant species at time intervals, with qualitative and quantitative (Scores) results. At the end of the experiment, a report of the changes over time. A ''score' is based on observing the well and is a coded value that means something like 'severe yellowing of stem' or "intense chlorosis of stem" with more than one score per well.
experiment type: colormetric assay
Inhibition makes well white, but experiment failure makes "milky white". A scientist can see the difference, but the automated reader can be fooled. so scientist marks specific wells as bogus BEFORE the results are read into the data system.
experiment type: kinetics
Each well is read multiple times over time and the calculated value is based on the multiple raw values. All raw data is saved or only the final derived value. Plot of the timed values.
experiment type: Ki values
Dose response experiment, with a ligand of known activity value and concentration recorded. The IC50, and further calculations based on IC50, ligand activity, and ligand concentration are saved for each sample.
experiment type: pooling and replicates
Pooled plates have replicated mixtures. Same as HTS and REP, but with a reference to mixture in Inventory.
experiment type: non-plate based experiments
Row of tubes, lawn format, and so on.
variable group
A candidate variable group is selected or built. The group should take account of the dimension variables to be used for the RFM upload and for the layout. The group should take account of the layout actual and placeholder values. The group should have clauclations from raw data and places to mark data invalid or promotable. The group might have a Review check.
layout
A candidate layout for the Well table is developed. The layout should have an actual concentration, actual well types for High/Low/Data/Reference wells, and placeholders for the sample IDs. The dimension, calculation, and set variables should work with the layout.
calculations
Candidate calculations are selected or made for the calculation variables of the variable group. The calculations need to follow the default or nondefault rules for calcs, especially for parent-child relations, performance, and using ASSOC, MATCHALL, and CONDITION= correctly.
reader file and format
An actual or realistic reader file is available from such an experiment, and the reader file sections are made and tested, and the sections assigned to variables of the variable group. Ordinals are assigned to Row and Column variables, along with dimensions that match the variable group and layout.
protocols and templates
Protocol assays or attributes might be added to the protocol. (Sections or templates might be built for the protocol display.) A protocol is selected or made, which contains the variable group, layout(s), and RFM formats, plus any required fields.
dictionaries and terms
Dictionary terms and dictionaries might be added for use in the protocol.
result tables
Result tables are selected or made, together with the result maps from the variable group to the result table(s).
experiments
The completed protocol is used to start an experiment, the layout and the RFM format are added, then the reader file(s) are selected. Any placeholders in the layout must be filled in. (Assembler). The experiment is calculated and stored.
analysis
The experiment is analyzed to determine if any data is invalid or questionable, and a recalculation and store occurs. A rule might be used for automatic checking for bad controls, dropped plates, and so on. The analysis might include a mod to a calc formula, data, or a change of model for a curve.
decision
The experiment is analyzed to determine if any samples are worthy of further experimentation. Such samples are marked for further testing. A score might be assigned based on rules.
review and/or release
A review is made (typically by a higher authority) of the results. That such a review has been made is noted somewhere. Perhaps the data is allowed to be used or seen by other workgroups, or is sent to corporate database.
browsing
Other persons at a company want to see a summary of the validated results, in a set format for all researchers, to avoid duplication and error.
Blood drug amount {bioavailability} relates to dose.
Brain-compartment drug concentration to blood-compartment drug concentration makes ratios {Blood-Brain Barrier penetration}.
Drug diffusion calculations {diffusivity} can measure drug-diffusion ease.
Distribution and elimination can combine {disposition, drug}.
Drugs have different concentrations in various body tissues {distribution, drug}.
Excretion {elimination, drug} uses urine and feces.
Ingested drugs affect intestinal-wall cells {enterocyte}.
Bile goes back to GI tract for recycling {enterohepatic cycling} (EHC).
Liver removes drugs from blood {intrinsic clearance} (CL).
Compounds above 500 to 700 cannot diffuse across lipid membrane {molecular weight theory}.
Percentage of orally administered drug in general blood circulation, or in urinary excretion, compares to intravenous administration {absolute oral bioavailability} {oral bioavailability}.
A vein {portal vein} carries blood to liver from GI tract.
Active or passive transport carries drug from intestine to portal vein {absorption, drug}.
Compounds absorbed from intestine {human intestinal absorption} (HIA) go to portal vein.
Intestines have contents travel rate {motility}.
Acidic or neutral drugs can diffuse across GI-tract lipid membrane, but basic drugs cannot diffuse {pH partition theory}.
Drug goes from intestine to portal vein {predicted fraction of human absorption} (Fa). Fraction is in percent.
Compounds can have good solubility in lipids {lipophilicity}.
Values {Fujita-Hansch pi value} can relate to lipophilicity.
Octanol/water partition coefficient logarithms {log P} can measure lipophilicity.
Hydrophobicity measures {molecular lipophilicity potential} (MLP) can calculate lipophilicity surface.
Lipophilic compounds can diffuse across lipid membrane {octanol-buffer partition coefficient theory}.
On brain-capillary endothelial-cell insides, proteins {P-glycoprotein} can prevent high-lipophilicity drugs from crossing BBB.
Drugs must get to sites {transport, drug} {drug transport}.
Diffusion carries molecules across membranes {passive transport}.
Drug breakdown by oxidation {drug metabolism} is mainly in liver.
Compounds can have an added group {adduct}.
Drug can inhibit or induce another drug {drug-drug interaction}.
Proteins {flavoprotein} can bind FAD or FMN.
Molecules {glutathione} (GSH) can participate in phase II conjugations.
Phase I oxidations, Phase II conjugations, and transport into bile reduce drug in hepatic blood {hepatic first-pass elimination} (HFPE).
Iron compounds {iron-oxene} {iron-oxenoid} can contain free oxygen atoms.
Drug metabolism makes products {metabolite}.
Nitrosoalkanes irreversibly bind to reduced heme intermediates of CYP450 enzymes {metabolite intermediate complexation}.
Compounds or forces can mutate genes {mutagenicity}.
Metabolism percentage {regioselectivity} categorizes sites as major, minor, or unobservable. Rate constant differences among sites cause metabolic-site regioselectivity.
Drugs can affect targets {selectivity, drug} and other sites.
Substrates {agonist} can bind to receptors and cause biologic response.
Substrates {antagonist, chemistry} can bind to receptor but cause no biologic response.
Hydrogen atoms can bind to carbon atoms {acetylation}.
Amino acids can bind to carboxylic-acid groups {amino acid conjugation}, on anti-inflammatory, hypolipidaemic, diuretic, and analgesic drugs.
Enzymes can change drugs to make them toxic {bioactivation}.
Drug metabolism has oxidations and reductions {biotransformation}.
Two charges can exchange {charge-transfer coupling} in reactions.
Molecules can attach small molecule {conjugation, molecule}.
Processes can make rings {cyclization}.
Glucuronic acid allows glucuronide formation {glucuronic acid conjugation}.
Molecules can conjugate with glutathione {glutathione conjugation} to form mercapturic acid.
Atoms {hydrogen bond acceptor} (HBA) can add hydrogen atom.
Atoms {hydrogen bond donor} (HBD) can release hydrogen atom.
Hydrogen atoms can abstract {hydrogen transfer}.
Hydrogen atoms can bind to oxygen atom {hydroxylation}.
Enzymes can change conformation to allow substrate binding {induced fit}.
Drugs can form complexes with receptors and then cause chemical or conformational changes {intrinsic activity, drug}.
Drug metabolism has oxidation or reduction {Phase I enzyme reaction}.
Drug metabolism has conjugation with small molecules {Phase II enzyme reaction}.
Hydrogen atoms removed from molecules {proton abstraction} can make water.
After ATP activates sulfate, sulfotransferase makes sulfate esters {sulfate conjugation}.
nucleophosphate energy compound {guanidine diphosphate} (GDP).
Energy molecules {uridine diphosphate} (UDP) can participate in phase II reactions.
Enzymes {adenylate cyclase} {adenylcyclase} can alter cAMP.
Enzymes {carboxylesterase} can catalyze phase I reactions.
Enzymes {cytochrome P-450} catalyze phase I reactions 3A4, 2D6, 2C9, 1A2, and 2E1.
Enzymes {epoxide hydratase} {epoxide hydrolase} can oxidize olefins and aromatics to make epoxide or oxirane metabolites. It can produce carcinogens.
Enzymes {glucuronyl-transferase} can catalyze phase II reactions, adding glucuronide to drugs.
Enzymes {glutathione-S-transferase}, in liver-cell cytoplasm, can catalyze phase II reactions to conjugate compounds to glutathione.
Enzymes {microsomal flavoprotein mono-oxygenase} can oxidize nitrogen or sulfur organics.
Enzymes {microsomal hydroxylase} can catabolize many compounds, mostly by oxidation, in endoplasmic reticulum.
Enzymes {mixed-function oxidase} (MFO) can catabolize many compounds, mostly by oxidation, in endoplasmic reticulum.
Enzymes {phospholipase A2} can catabolize lipids.
Enzymes {phospholipase C} can catabolize lipids.
Enzymes {protein kinase} can catabolize proteins.
Enzymes {uridine diphosphoglucose transferase} {uridine-diphosphate-glucuronosyl-transferase} (UDP-GT) (UGT) can catalyze phase II reactions, adding glucuronide to drugs.
Chemicals can inhibit drugs {drug inhibition}. Inhibitor has binding constant.
Inhibitor can bind to non-active site {allosteric non-competitive inhibition}.
Drugs {entry inhibitor} can prevent viruses from entering cells.
Drugs {integrase inhibitor} can prevent virus DNA from inserting into host DNA.
Drugs {maturation inhibitor} can block gag-protein protease receptor, so gag protein is not split, and HIV virus coat is not made. PA-457 comes from betulinic acid from Taiwan herb, plane trees, and birch trees.
Metabolized compounds can bind to enzymes {mechanism-based inhibition}.
Drugs {protease inhibitor} can inhibit protease enzymes.
Most-reactive electron {highest occupied molecular orbital} (HOMO) can be in electron-rich nucleophilic molecules.
Most-reactive electron {lowest unoccupied molecular orbital} (LUMO) can be in electron-poor electrophilic molecules.
Total metabolism has rate {absolute metabolism rate}.
Reaction rate typically depends on concentration and temperature {enzyme kinetics}.
Metabolism rate at site has estimated ease {lability}.
Enzymes have binding constants {Michaelis-Menten constant} (Km).
Sites {labile site} can have high metabolism rate and low activation energy.
Sites {moderate site} can have intermediate metabolism rate and activation energy.
Sites {stable site} can have low metabolism rate and high activation energy.
Active compounds have small clusters {asymmetric set} in compound space.
Automated assays {biological screening} can identify promising compounds from compound libraries.
Active compounds have small clusters {embedded set} in compound space.
Sample collections {inventory, sample} {sample inventory} can be ready for testing, stored in plate wells.
From many compounds, processes {lead finding} {lead generation} {lead selection} can identify compounds that have significant chemical activity.
Processes {lead optimization} can efficiently identify structure-activity relationships for generated leads.
Sample points {outlier} can be far from expected values.
Samples can go to further testing {promotion}.
Drug-receptor geometry {drug structure} is a physico-chemical property and can be quantitative.
structure-activity relationships
Drugs have structure-activity relationships (SAR), which can be quantitative (QSAR). Drugs have property-activity relationships.
activity
Drug activity equals physicochemical-variable function. Drug activity relates to concentration, partition coefficient, or product formation. Stages have probabilities. Drug activity is proportional to concentration product, complexing probability, changing probability, and partitioning probability.
activity: complex formation
Drugs form complexes with receptors {intrinsic activity, complex}. Drugs {chemotherapeutic drug} can cause chemical reactions or conformational changes. Drugs {pharmacodynamic drug, complexes} can make complexes but do not change conformation or cause reactions.
Complex-formation probability is formation-reaction equilibrium constant. Equilibrium constant depends on both equilibrium type and substituent electronic influence on reaction center. log(K) = k1 * sigma + k2 {linear free energy equation, structure} (LFE). log(1 / concentration) = k1 * sigma + k2. Electronic influences are universal and have tables of values. Equilibrium type results from multiple regression analysis of simultaneous equations.
activity: partitioning
If hydrophobicity affects drug structure, partition coefficient affects activity. log(K) = k3 * pi + k1 * sigma + k2 and log(1 / concentration) = k3 * pi + k1 * sigma + k2. Partition coefficients are universal and have tables of values.
activity: transport
Drugs have to get to target site. Drug transport involves diffusion, active transport, adsorption, binding to serum proteins, or membrane interactions. Mechanisms that oppose drug transport are excretion, metabolism, and localization in fat. Excretion is faster for hydrophilic. Metabolism is faster for hydrophobic. Localization in fat is faster for hydrophobic. Drug transport affects drug activity. log(K) = k3 * pi + k1 * sigma + k2 - k4 * pi^2. log(1 / concentration) = k3 * pi + k1 * sigma + k2 - k4 * pi^2. Drug transport factors are universal and have tables of values.
structure
Molecule structure depends on atom types, atom numbers, chemical bonds, spatial relations, and atom locations. Features are either present or absent, with no interactions.
structure: molecular connectivity indices
Kier and Hall used features such as electrotopologic state index, valence, molecular shape and flexibility {kappa index, structure}, branching, unsaturation, cyclization, and heteroatom position. They found molecular connectivity indices, based on Randic's branching index, calculated from hydrogen-suppressed chemical graph or skeleton structure. For example, atoms can have number of sigma electrons contributed {simple delta index, structure} or number of valence electrons {valence delta}.
structure: molecular orbital
Quantum-mechanical structure description uses molecular orbital (MO) theory. Molecular orbitals depend on electron location and energy. Total conformation energy gives probability. MO typically ignores solvents.
Highest occupied molecular orbital gives the most-reactive electron for electron-rich nucleophilic molecules. Lowest unoccupied molecular orbital gives the most-reactive electron for electron-poor electrophilic molecules.
MO can test reaction paths and find thermodynamic information, by checking energies in different configurations.
Molecular orbitals can be linear combinations of atomic orbitals (LCAO). Atomic-orbital contribution probability is linear-coefficient squared, and point charge is probability sum.
structure: interactions
Comparative Molecular Field Analysis (CoMFA) uses partial least-squares to analyze grid around site atom and find grid-point hydrophobic, electrostatic, and steric interactions.
structure: ab initio
Ab initio analysis uses electron locations to find charges, electrostatic potentials, dipole moments, ionization energies, electron affinity, and activation energies. Semiempiric analysis uses only valence electrons and parameterizes core electrons. Modified neglect of differential overlap (MNDO) ignores overlaps. Perturbative configuration interaction using localized orbitals (PCILO) uses perturbations. Varying bond angles, bond lengths, and torsion angles can find minimum energy and preferred conformation.
structure: axial-equatorial configuration
Non-conjugated-ring substituent positions can be in ring plane {equatorial configuration} or perpendicular {axial configuration}.
structure: branching
Carbon chain can have fork {branching}.
structure: ionization degree
Molecule can have charge {degree of ionization} {ionization degree}.
structure: dipole moment
Opposite charges can separate by distance.
structure: electrostatic potential
Electric potential energy comes from electric field.
structure: molecular similarity
Molecules can be similar in 3D atomic configuration, atom pairs, chemical graphs, electron densities, field potentials, molecular fragments, molecular properties, molecular surfaces, steric volumes, or topological/information theory indexes.
structure: orientation
Molecule spatial alignment is at receptor site.
structure: radical
Atoms can have one electron in outer orbital.
structure: singlet or triplet state
Orbital state can have paired electrons {singlet state}. Orbital state can have unpaired electrons {triplet state}.
Connection tables number non-hydrogen atoms, name atomic elements, name atom number to which they connect, and name atom types {Chemical Abstracts Service} (CAS).
Molecules can be vectors, including chemical activity, in abstract space {Chemical Descriptor Space} (CDS).
Base compounds {building block} can attach one to four small molecules {combinatorial chemistry} to add functional groups and make compound libraries with molecular weights 300 to 750.
Tables {connection table} can describe three-dimensional structures.
Matrices {connectivity matrix} can graph molecular connections.
Electrostatic fields make potentials {Coulombic potential}.
Polar solute can cross lipid membrane if hydrogen bonds to water break {desolvation}. Polar solute with fewer hydrogen bonds to water and lower hydrogen-bonding potentials can diffuse more easily.
Indexes {electrotopologic state index} can depend on topology structures.
Molecular markers {encoding tag} can track combinatorial-chemistry molecules.
Molecule atoms {hetero} can be not carbon C or hydrogen H. Hetero can refer to solvent, non-solvent, water, ion, or ligand atoms.
Compounds {heterocyclic compound} can have rings with atoms other than carbon.
Molecular regions can repel water {hydrophobicity}.
Cytochrome P450 has types {isoform}.
Combinatorial chemistry makes compound permutations {library of compounds}.
Tables {nearest neighbor table} can rank different compounds by similarity.
Superimposed molecules show constants across diverse molecules and so identify sites and reactions {pharmacophore}.
Molecules have atomic properties, functional groups, and molecular properties {similarity matrix}.
Oxygen can have positive charge {superoxide anion}.
Possible compound permutations can be in database {virtual compound library}.
Strings {Wiswesser line notation} (WLN) can uniquely describe three-dimensional structure.
X-ray crystallography patterns {X-ray structure} can indicate atom positions.
Methods {validation methods} can check structure-activity relationship correlations, predictions, and designs.
Validation methods {bootstrapping validation method} can use only internal data.
Methods {cross-validated correlation coefficient} can validate and predict data.
For all data subsets, algorithms {cross-validation} (CV) can remove one data subset and calculate remainder.
Other data can pair with model to predict activity {external validation}.
Validation methods {Fisher F-test} can use F test.
Validation methods {fitness function} (FIT) can measure fit.
cross-validation method {jackknife validation method, drug}.
Methods {lack-of-fit} (LOF) can measure fit.
cross-validation method {leave-groups-out, drug} (LGO).
cross-validation method {leave-one-out, drug} (LOO).
Methods {predictive residual sum of squares, drug} (PRESS) can measure fit.
cross-validation method {scrambling dependent Y-values, drug}.
Methods {standard deviation method, drug} (sPRESS) can measure fit.
Methods {standard error of predictions, drug} (SDEP) can measure fit.
Methods {standard error of regression, drug} can measure fit.
Outline of Knowledge Database Home Page
Description of Outline of Knowledge Database
Date Modified: 2022.0225