| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies the design, synthesis, optimization, and biological evaluation of molecules intended to modulate biological systems for therapeutic use; excludes purely biological pharmacology without chemical intervention. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from molecular and atomic interactions (binding, reactivity, stereochemistry) to cellular, organ-level, and organism-level pharmacodynamic and pharmacokinetic behavior. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Small molecules, drug candidates, lead compounds, metabolites, receptors, enzymes, transporters, cofactors, prodrugs, pharmacophores, bioisosteres, ADMET species. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Affinity, potency, selectivity, lipophilicity, solubility, permeability, metabolic stability, toxicity, stereochemistry, conformational preferences, redox characteristics. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Drug classes, target families (GPCRs, kinases, ion channels), pharmacophores, bioisosteric groups, ADMET categories, structural alerts, prodrug types, reactive metabolite classes. |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Concentration, pH, ionic strength, logP/logD, binding constants, metabolic turnover rates, plasma protein binding, clearance, half-life, receptor occupancy, redox state. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via QSAR descriptors, pharmacophore maps, binding constants, pKa values, ADMET parameters, docking scores, metabolic rate constants, free-energy profiles. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Ideal receptor–ligand fit, isolated binding sites, minimal off-target activity, simplified ADMET models, static conformers, linear free-energy assumptions, single-pathway metabolism. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Hold in controlled in vitro systems; break down in vivo where metabolism, protein binding, off-target effects, conformational ensembles, and nonlinear pharmacokinetics dominate. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Chemical structure reliably predicts biological activity; functional groups behave consistently in biological contexts; SAR trends are transferable. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes interpretable binding pockets, drug-like space is navigable, metabolic pathways are predictable, and structural modifications produce rational changes in ADMET behavior. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires compatibility among SAR data, pharmacophore models, binding-site structures, ADMET predictions, and observed biological activity. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands coherence between chemical structure, mechanistic pharmacology, metabolism, toxicity, and therapeutic outcome across all biological levels. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Binding signals, enzyme inhibition, receptor activation/inactivation, cell viability changes, pharmacokinetic curves, metabolic transformations, toxicity markers, fluorescence/absorbance shifts. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by assay sensitivity, low-affinity binders, weak fluorescence, low metabolite abundance, rapid clearance, noise in biological assays, and off-target interference. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | IC₅₀/EC₅₀, Ki/Kd, % inhibition, logP/logD, clearance (mL/min/kg), half-life (h), bioavailability (%), binding occupancy (%), metabolic rate (min⁻¹), concentration (nM–µM). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Plate readers, fluorescence/luminescence detectors, LC-MS/MS, NMR, SPR, ITC, flow cytometers, high-content imaging systems, metabolic stability rigs, automated dose–response platforms. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Potency via IC₅₀/EC₅₀; affinity via Kd/Ki; metabolic stability by half-life; permeability by PAMPA/Caco-2; toxicity via LD₅₀/viability assays; solubility by shake-flask or kinetic methods. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Dose–response assays, enzyme inhibition assays, cell-based functional assays, metabolite profiling, in vitro ADMET tests, standardized plate workflows, buffer and pH control, replicate runs. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Time-course concentration sampling, multiple-dose replicates, metabolic clearance monitoring, automated screening campaigns, SPR binding curves, LC-MS/MS quantification, toxicity time courses. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Biological replicates, technical replicates, multiple cell lines, tissue distribution sampling, plasma sampling across timepoints, replicate injections in LC-MS/MS, protein-binding sampling. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Dose–response curves, binding isotherms, metabolic degradation curves, toxicity plots, chromatograms, MS traces, NMR spectra, fluorescence/time-series data, imaging data, ADMET panels. |
| | Resolution | The granularity or precision with which data is captured. | Determined by detector sensitivity, plate-reader resolution, instrument noise, MS mass accuracy, imaging pixel resolution, SPR angular precision, sampling frequency, and assay variability. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Instrument calibration (MS, plate readers, SPR), standard curves for concentration, control wells, reference compounds, pH meter calibration, temperature control validation, detector linearity checks. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Noise, baseline drift, pipetting error, off-target interference, compound instability, protein-binding artifacts, fluorescence quenching, sample carryover, assay-lot variability. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Binding signals, enzyme inhibition, receptor activation/inactivation, cell viability changes, pharmacokinetic curves, metabolic transformations, toxicity markers, fluorescence/absorbance shifts. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Limited by assay sensitivity, low-affinity binders, weak fluorescence, low metabolite abundance, rapid clearance, noise in biological assays, and off-target interference. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | IC₅₀/EC₅₀, Ki/Kd, % inhibition, logP/logD, clearance (mL/min/kg), half-life (h), bioavailability (%), binding occupancy (%), metabolic rate (min⁻¹), concentration (nM–µM). |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Plate readers, fluorescence/luminescence detectors, LC-MS/MS, NMR, SPR, ITC, flow cytometers, high-content imaging systems, metabolic stability rigs, automated dose–response platforms. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Potency via IC₅₀/EC₅₀; affinity via Kd/Ki; metabolic stability by half-life; permeability by PAMPA/Caco-2; toxicity via LD₅₀/viability assays; solubility by shake-flask or kinetic methods. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Dose–response assays, enzyme inhibition assays, cell-based functional assays, metabolite profiling, in vitro ADMET tests, standardized plate workflows, buffer and pH control, replicate runs. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Time-course concentration sampling, multiple-dose replicates, metabolic clearance monitoring, automated screening campaigns, SPR binding curves, LC-MS/MS quantification, toxicity time courses. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Biological replicates, technical replicates, multiple cell lines, tissue distribution sampling, plasma sampling across timepoints, replicate injections in LC-MS/MS, protein-binding sampling. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Dose–response curves, binding isotherms, metabolic degradation curves, toxicity plots, chromatograms, MS traces, NMR spectra, fluorescence/time-series data, imaging data, ADMET panels. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Determined by detector sensitivity, plate-reader resolution, instrument noise, MS mass accuracy, imaging pixel resolution, SPR angular precision, sampling frequency, and assay variability. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Instrument calibration (MS, plate readers, SPR), standard curves for concentration, control wells, reference compounds, pH meter calibration, temperature control validation, detector linearity checks. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Noise, baseline drift, pipetting error, off-target interference, compound instability, protein-binding artifacts, fluorescence quenching, sample carryover, assay-lot variability. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling dose, concentration, solvent, pH, temperature, enzyme/cofactor levels, cell-line choice, and expression systems to test binding, activity, metabolism, and toxicity hypotheses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring natural metabolic fate, spontaneous degradation, non-specific binding, transporter behavior, distribution profiles, and receptor/signaling responses without forced intervention. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted potency, selectivity, metabolic liability, and ADMET behavior against in vitro/in vivo assays, biochemical binding data, PK curves, and toxicity screens. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating dose–response assays, enzyme inhibition assays, cell viability tests, metabolic stability runs, PK sampling, LC–MS/MS quantification, and imaging-based phenotypic assays. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Extracting IC₅₀, EC₅₀, Kd, Ki, clearance, half-life, bioavailability, partition coefficients, and toxicity thresholds; fitting PK/PD models; analyzing outlier behavior and SAR deviations. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating SAR models, QSAR/QSPR frameworks, docking predictions, ADMET models, PK/PD models, and toxicity classifiers based on predictive accuracy, parsimony, interpretability, and robustness. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying assay noise, spectral interference, pipetting errors, plate effects, compound instability, off-target effects, biological variability, and LC–MS/MS quantification errors. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing sample positions, blinding biological readouts, standardizing assay conditions, controlling batch-to-batch variability, verifying compound purity, and eliminating operator bias. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of SAR trends, PK/PD fits, docking outputs, mechanism claims, metabolite identification, potency/selectivity assertions, and toxicity interpretations. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating pharmacophore models, revising SAR frameworks, reinterpreting metabolic pathways, adjusting PK/PD assumptions, changing target-binding models based on new biological evidence. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of assay conditions, compound sourcing, purification, purity data, calibration methods, modeling assumptions, PK/PD fitting parameters, and statistical treatment of data. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of negative results, toxicity risks, assay limits, model uncertainties, animal-study details, and ensuring reproducibility and responsible experimental and clinical conduct. |