ADMET in Drug Discovery: Absorption, Distribution, Metabolism, Excretion, Toxicity

Table of Contents

A potent compound is just the starting point. To become a drug, it has to be absorbed, reach the target tissue, persist long enough to act, be eliminated safely, and avoid toxic effects. ADMET — Absorption, Distribution, Metabolism, Excretion, Toxicity — describes these properties. ADMET failures account for a major share of late-stage drug attrition.

A: Absorption

Most drugs are taken orally and must cross the intestinal wall to reach the bloodstream. Key factors:

  • Solubility: Drug must dissolve in GI fluids. Poorly soluble compounds have erratic absorption
  • Permeability: Crossing the intestinal epithelium. Lipinski’s rule of five (MW < 500, logP < 5, HBD ≤ 5, HBA ≤ 10) historically guides oral drug-likeness
  • P-glycoprotein efflux: P-gp pumps drugs back out of cells. P-gp substrates often have low oral bioavailability
  • First-pass metabolism: Liver metabolism on first passage from the gut

Common assays: Caco-2 monolayer permeability, PAMPA (parallel artificial membrane permeability assay), kinetic solubility, animal PK with oral and IV dosing.

D: Distribution

Once in the bloodstream, drug distributes between plasma and tissues. Important parameters:

  • Volume of distribution (Vd): Apparent volume the drug occupies. High Vd suggests tissue binding
  • Plasma protein binding: Most drugs bind albumin or α1-acid glycoprotein. Only the unbound fraction is pharmacologically active
  • Tissue penetration: Drug reaching the site of action — particularly critical for CNS drugs (blood-brain barrier) and antitumor agents

M: Metabolism

Most drugs are metabolized in the liver, primarily by cytochrome P450 enzymes. Considerations:

  • Metabolic stability: How fast the drug is metabolized. Determines half-life and dosing frequency
  • Major metabolic enzymes: CYP3A4 (most drugs), CYP2D6, CYP2C9, CYP2C19, CYP1A2
  • Drug-drug interactions: Inhibition or induction of CYP enzymes affects co-administered drugs
  • Active metabolites: Some metabolites contribute to or replace the parent drug’s activity

Common assays: Liver microsome stability, hepatocyte metabolism, CYP inhibition panel, time-dependent inhibition assays.

E: Excretion

How drug or metabolites leave the body:

  • Renal excretion: Filtration, secretion, and reabsorption in kidneys
  • Biliary excretion: Conjugated drug or metabolites secreted into bile
  • Clearance (CL): Volume of plasma cleared of drug per unit time. Combined with Vd, determines half-life

Renal impairment changes excretion of many drugs and often requires dose adjustment.

T: Toxicity

The most heterogeneous category. Common ADMET-related toxicities:

  • Hepatotoxicity: Drug-induced liver injury (DILI) — a major cause of withdrawal
  • Cardiotoxicity: hERG channel inhibition leads to QT prolongation and torsades de pointes. hERG is screened early in drug discovery
  • Genotoxicity: Mutagenic potential — Ames test, micronucleus assay
  • Reproductive toxicity: Pregnancy and developmental effects
  • Immunogenicity: Especially for biologics
  • Off-target pharmacology: Activity at unintended targets

In silico ADMET prediction

Computational tools predict ADMET properties from structure:

  • SwissADME: Free, web-based property prediction
  • QikProp (Schrödinger): Commercial tool widely used in industry
  • ADMET-AI, Chemprop: Deep learning predictors
  • PubMed/ChEMBL: Pull data on related compounds

Predictions are most accurate for chemical scaffolds similar to training data. Always validate experimentally before committing.

Practical strategy

  • Build ADMET filters into early hit identification — don’t pursue compounds with obvious liabilities
  • Run rapid in vitro assays (microsomes, hERG, kinetic solubility) on every series
  • Generate animal PK data to validate in vitro predictions before committing to lead optimization
  • Track structure-property relationships alongside structure-activity relationships

ADMET is where many promising compounds die. Building ADMET awareness into early drug discovery — not waiting until lead optimization — saves time, money, and gives you the best chance of nominating a development candidate that will survive the clinic.

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