07 — PK/PD Modeling, Human Dose Projection & Toxicity Evaluation
Overview
Quantitative pharmacology — PK/PD modeling, simulation, and dose projection — bridges non-clinical to clinical development for ADCs. Because ADCs generate multiple analytes with distinct PK behaviors and distinct drivers of efficacy vs. toxicity, the modeling framework is substantially more complex than for small molecules or naked antibodies. Regulatory agencies (FDA, EMA) increasingly expect mechanistic or semi-mechanistic PK/PD analyses at IND and NDA/BLA submission.
1. ADC PK Modeling Framework
1a. Multi-Analyte PK Structure
A minimally adequate ADC PK model must account for the distinct disposition of at least three analytes:
Dose (ADC)
│
▼
[TAb PK model] —— (deconjugation rate, kdc) ——→ [naked Ab accumulates]
│
▼
[cAb PK model] —— (linker catabolism, kcat) ——→ [conjugated payload released]
│
▼
[Free payload PK model] ←— (also from systemic deconjugation in plasma)
Common modeling approach: Fit TAb and cAb separately, with a first-order deconjugation parameter (kdc) relating cAb decline to TAb. Then fit free payload as a metabolite model driven by both (a) systemic ADC deconjugation and (b) intracellular processing with first-pass release.
1b. Two-Compartment Antibody PK Model
For the mAb backbone (TAb), a standard two-compartment model applies:
dA_central/dt = −(CL + Q) × C_central + Q × C_peripheral − kdc × A_central
dA_peripheral/dt = Q × (C_central − C_peripheral)
Where:
- CL = total clearance (FcRn-mediated recycling + non-specific proteolysis + target-mediated)
- Q = inter-compartmental clearance
- kdc = first-order deconjugation rate (constant or DAR-dependent)
- V_central, V_peripheral = volumes
Typical IgG1 parameters (human):
| Parameter | Value | Notes |
|---|---|---|
| CL | 0.2–0.5 mL/h/kg | Includes FcRn recycling; longer for optimized Fc |
| V_central | 40–60 mL/kg | ~plasma volume (IgG1 poorly distributed) |
| V_ss | 80–130 mL/kg | Limited tissue distribution due to size |
| t½ | 14–21 days | Varies with target expression, ADA, TMDD |
1c. DAR Kinetics (Deconjugation Modeling)
DAR declines over time as payload is lost from the antibody in circulation. Several modeling approaches:
Approach 1 — Empirical first-order:
cAb(t) = TAb(t) × DAR₀/DAR_max × exp(−kdc × t)
where kdc is estimated from the ratio of cAb vs. TAb over time. Simplest; adequate for cleavable linkers with relatively rapid deconjugation.
Approach 2 — DAR-distribution model (stochastic conjugation): Stochastic conjugation (lysine or cysteine) produces a mixture of DAR species (DAR0, 2, 4, 6, 8). Each DAR species has a different clearance rate (higher-DAR species often clear faster due to hydrophobicity → FcγR-mediated uptake). A mixture model tracks each DAR species separately:
∑ DAR_i × fraction_i(t) = observed average DAR(t)
where each fraction_i follows independent PK with different clearance rates. More mechanistic; useful when DAR-specific PK is important (e.g., DAR8 species of T-DXd clear faster than DAR4).
Approach 3 — Mechanistic linker stability model: For cleavable linkers, deconjugation is modeled as a rate function of linker hydrolysis and enzymatic cleavage kinetics in plasma:
kdc = k_hydrolysis × f(pH, temperature) + k_enzyme × [enzyme]
Most complex; rarely used in regulatory submissions but useful for linker optimization.
1d. Target-Mediated Drug Disposition (TMDD)
When the ADC target antigen is expressed on non-tumor cells or shed into circulation, TMDD significantly affects PK:
Full TMDD model (Mager-Jusko):
dC/dt = Rate_in − (CL/V) × C − kon × C × R + koff × RC
dR/dt = ksyn − kdeg × R − kon × C × R + koff × RC
dRC/dt = kon × C × R − (koff + kint) × RC
Where R = free receptor/antigen, RC = bound complex, kint = internalization rate.
Quasi-steady-state (QSS) approximation: Used when binding/unbinding is fast relative to PK:
RC = C × R_total / (KSS + C)
where KSS = (koff + kint)/kon
TMDD is clinically important for:
| ADC | TMDD driver | Clinical manifestation |
|---|---|---|
| Gemtuzumab ozogamicin | CD33 on circulating AML blasts and monocytes | Dose-proportional PK only when blast burden reduced |
| T-DM1 (Kadcyla) | Shed HER2 ECD (sHER2) | Elevated sHER2 → faster ADC clearance; non-linear PK at low doses |
| Sacituzumab govitecan | Shed TROP-2 | Less pronounced; TROP-2 shedding rate lower than HER2 |
| Inotuzumab ozogamicin | CD22 on circulating B cells | Dose-dependent PK; blast burden covariate in popPK |
1e. Population PK (popPK) Models for ADCs
PopPK models characterize PK variability across patients and identify covariates explaining that variability. Submitted in NDA/BLA to support labeling and dose adjustment.
Covariates commonly evaluated in ADC popPK:
| Covariate | Analyte | Expected Effect | Clinical Guidance |
|---|---|---|---|
| Body weight / BSA | TAb, cAb | Primary driver of Vd and, to a lesser extent, CL | Supports weight-based (mg/kg) vs. flat dosing decision |
| ADA status | TAb, cAb | Positive ADA → elevated CL (immune complex clearance) | Flag ADA+ subjects in PK analysis; may exclude from E-R if large impact |
| Baseline sHER2 (for HER2 ADCs) | TAb, cAb | High sHER2 → ~30–50% faster CL in some analyses | Covariate in T-DM1 popPK; used to explain lower exposure in high-sHER2 patients |
| Hepatic function (ALT, bilirubin) | Free payload | Payload CL reduced in hepatic impairment (CYP3A4/5 substrates) | Dose reduction guidance for moderate/severe hepatic impairment |
| Renal function (GFR, CrCl) | Free payload | Minor to moderate effect for most payloads | Generally no dose adjustment needed unless payload is renally cleared |
| Tumor burden (size, lesion count) | cAb | High tumor burden → faster antigen-mediated clearance | Descriptive covariate; not always included in final model |
| Albumin (baseline) | TAb | Low albumin → FcRn competition → slightly altered antibody PK | Included in some models as exploratory covariate |
Software: NONMEM (most common regulatory submission tool), Monolix (SAEM algorithm), Phoenix NLME. Estimation methods: FOCE-I (NONMEM), SAEM. Diagnostics: VPC (Visual Predictive Check), GOF plots, bootstrap CIs.
2. PK/PD Modeling — Efficacy
2a. Tumor Growth Inhibition (TGI) Models
Non-clinical (xenograft): The standard PK/PD model for ADC efficacy in mouse xenograft:
dW/dt = (kg − kd × C_payload) × W
Where:
- W = tumor volume (mm³)
- kg = tumor growth rate constant
- kd = drug-effect (kill) rate constant
- C_payload = intracellular free payload concentration (estimated from plasma cAb via a cellular disposition submodel)
More mechanistic models include:
- Two-compartment tumor: growing fraction + resting fraction (Simeoni model)
- Transit compartment model for drug effect delay
- Bystander effect: separate kill rate for antigen-negative neighbors driven by diffusible payload
Translational challenge: Mouse tumor xenografts have different antigen density, cathepsin B activity, and tumor architecture than human tumors. Allometric or empirical scaling must be applied when translating TGI parameters to clinical tumor size endpoints.
2b. Indirect Response Models (Clinical PD)
For cytostatic effects or biomarker-based PD:
dR/dt = kin × (1 − Imax × C/(IC₅₀ + C)) − kout × R
Where R = biomarker (tumor size, serum marker), kin/kout = synthesis/degradation, Imax = maximum inhibition, IC₅₀ = half-maximal concentration of the relevant PK driver.
2c. Exposure–Response (E-R) for Efficacy
Clinical E-R is used to:
- Confirm dose selection (is the chosen dose in the efficacious exposure range?)
- Support alternative dosing regimens (Q2W vs. Q3W; flat dose vs. weight-based)
- Characterize special populations
Typical E-R metrics for ADC efficacy:
| Exposure Metric | Efficacy Endpoint | ADC Example |
|---|---|---|
| cAb AUC₀–τ (cycle 1) | ORR, PFS, tumor shrinkage | T-DM1, T-DXd |
| cAb Ctrough | DCR, depth of response | Brentuximab vedotin |
| Cumulative cAb AUC | OS in some models | — |
3. PK/PD Modeling — Toxicity
3a. Free Payload as Toxicity Driver
Off-target toxicity is primarily driven by free payload systemic exposure (not ADC exposure):
| Toxicity | Payload | Exposure Metric | Evidence |
|---|---|---|---|
| Peripheral sensory neuropathy | MMAE | Cumulative AUC (free MMAE) | E-R in vedotin programs; threshold ~AUC > X µg·h/mL cumulative |
| Myelosuppression (neutropenia) | MMAE, DXd, SN-38 | Cmax (free payload) | Direct myeloid progenitor cytotoxicity |
| Diarrhea/GI toxicity | SN-38 | AUC (free SN-38) | Sacituzumab govitecan; SN-38 AUC in GI epithelium |
| ILD/pneumonitis | DXd | Unclear PK-driver; possibly lung tissue DXd AUC | T-DXd clinical data; E-R not clearly established for Cmax or AUC |
| Hepatotoxicity (VOD/SOS) | Calicheamicin | Cmax (free calicheamicin) | Mylotarg; rapid DSB in hepatocytes after Cmax peak |
| Ocular toxicity (microcyst) | MMAF | Cumulative dose/AUC | Blenrep; MMAF accumulation in corneal epithelium |
| Thrombocytopenia | DM1 (catabolite) | AUC (Lys-SMCC-DM1) | T-DM1 mechanism: DM1 in MK lineage impairs platelet production |
3b. Semi-Mechanistic Myelosuppression Models
The Friberg model (transit compartment model for neutrophil dynamics) is commonly applied to ADC-induced neutropenia:
dProl/dt = kin × (1 − E(t)) × Prol × (Circ₀/Circ)^γ − k_tr × Prol
dT₁/dt = k_tr × Prol − k_tr × T₁
dT₂/dt = k_tr × T₁ − k_tr × T₂
dT₃/dt = k_tr × T₂ − k_tr × T₃
dCirc/dt = k_tr × T₃ − k_circ × Circ
E(t) = Slope × C_payload(t) [linear E-R for neutrophil inhibition]
Where Prol = proliferating cells, T₁–T₃ = transit/maturation compartments, Circ = circulating neutrophils, Circ₀ = baseline ANC, γ = feedback parameter, E = drug effect.
This model has been applied to:
- Brentuximab vedotin (MMAE-driven neutropenia)
- Sacituzumab govitecan (SN-38-driven neutropenia)
- T-DXd (DXd-driven neutropenia)
3c. Platelet Model for T-DM1
T-DM1 causes thrombocytopenia by a unique mechanism: DM1 catabolite (Lys-SMCC-DM1) inhibits megakaryocyte (MK) proplatelet formation, not by killing MKs but by disrupting tubulin dynamics in MK proplatelet extensions. A modified transit model with an MK compartment has been published for T-DM1.
4. Human Dose Projection
4a. NOAEL → First-in-Human (FIH) Starting Dose
Standard regulatory approach (FDA Estimating the Maximum Safe Starting Dose guidance, 2005; EMEA MABEL guidance, 2007):
Step 1 — Identify NOAEL in most sensitive species (usually monkey for mAb/ADC):
- NOAEL from GLP 4-week repeat-dose toxicology study in cynomolgus monkey
- Units: mg/kg
Step 2 — Human Equivalent Dose (HED):
HED = NOAEL (animal) × (BW_animal / BW_human)^(1 − 0.75)
= NOAEL × (BW_animal / BW_human)^0.25
For monkey-to-human: approximately divide NOAEL by ~1.8 (based on 5 kg monkey, 60 kg human on a BSA-normalized basis) — but for mAbs/ADCs, mg/kg scaling is preferred (not BSA scaling) because FcRn-mediated clearance scales more closely with body weight than BSA.
Step 3 — Apply safety factor:
- Typical: HED / 10 = starting dose
- For ADCs with novel or highly toxic payloads, safety factor may be 1/30 to 1/50
Step 4 — Confirm starting dose against MABEL (for immunomodulatory or high-potency agents):
- MABEL = minimum dose expected to produce a measurable biological effect
- For ADCs: MABEL often estimated from receptor occupancy at target IC₅₀ or from minimal TGI in xenograft models
- Starting dose = lower of NOAEL/10 and MABEL-based dose
4b. Allometric Scaling of PK Parameters
For non-mAb components (free payload PK in non-clinical):
Simple allometry (power function):
CL = a × BW^0.75
Vd = b × BW^1.0
Where a and b are fitted from mouse, rat, monkey data. Projects human CL and Vd.
Fixed exponents (for mAb components):
- Antibody CL: exponent 0.85–0.9 (empirically derived for IgG)
- Antibody Vd: exponent ~1.0
- Because FcRn-mediated recycling dominates mAb CL, neonatal FcRn expression level must be confirmed as a scaling anchor
Two-species approach (Obach & Reed-Hagen method): Use monkey and rat for two-point allometry when mouse/rat data are unreliable (due to anti-mouse cross-reactivity or TMDD in rodents from cross-reactive antigen).
4c. PK/PD-Driven Dose Projection
More sophisticated approach integrating non-clinical TGI data:
- Fit non-clinical TGI model (xenograft) → estimate Cdrug_threshold (minimum cAb or payload concentration for tumor stasis/regression)
- Scale non-clinical PK to human (allometry)
- Simulate human concentration-time profiles at various doses
- Identify dose that achieves human C_threshold ≥ threshold for ≥X% of dosing interval
- Cross-check against safety margin from NOAEL
This approach has been used for T-DXd dose selection (8 mg/kg Q3W selected in part because simulations showed PK above in vivo effective exposure for >21 days in most patients).
5. Toxicity Evaluation Framework
5a. Non-Clinical Safety Studies Supporting IND (ICH M3(R2) / ICH S9)
For anticancer ADCs (ICH S9 scope — serious/life-threatening indication):
| Study | Species | GLP | Key Readouts |
|---|---|---|---|
| Single-dose (dose range finding) | Rat + monkey | No | MTD, NOAEL, clinical signs, body weight |
| 4-week repeat-dose + 4-week recovery | Cynomolgus monkey | Yes (pivotal) | NOAEL, reversibility, TK (PK at tox doses) |
| 4-week repeat-dose | Rat (if rat pharmacologically relevant) | Yes | Species comparison; may be replaced by monkey if not relevant |
| Genetic toxicology | In vitro ± in vivo | Yes | Payload-driven genotoxicity (most cytotoxic payloads are clastogenic) |
| Safety pharmacology | CV, CNS, respiratory | Yes | mAb backbone safety; payload CNS effects (MMAE: neuropathy) |
ICH S9 flexibility for oncology: Carcinogenicity and reproductive/developmental toxicology studies not required for advanced-cancer indications at IND stage (evaluated post-approval for adjuvant/curative settings per ICH S9).
5b. Toxicokinetics (TK)
TK samples collected alongside toxicology studies to characterize systemic exposure at each dose level:
- Minimum TK design: Day 1 and last dosing day of each study phase; 5–8 time points per profile
- Analytes: TAb (minimum), cAb, free payload
- TK parameters: AUC₀–τ, Cmax, t½ (from terminal phase if sufficient points)
- TK/PD correlation: NOAEL dose → TAb/cAb AUC at NOAEL = human exposure target for safety margin calculation
Safety margin calculation:
Safety margin = AUC (NOAEL in monkey) / AUC (human at proposed clinical dose)
Typically ≥3–10× margin required for oncology ADCs (lower acceptable margin than non-oncology drugs per ICH S9).
5c. Off-Target Tissue Distribution (Tissue Cross-Reactivity, TCR)
TCR studies use the clinical ADC antibody component (or the naked antibody) on a panel of human normal tissues (37 tissue panel per FDA) using IHC:
- Identifies unexpected antigen expression in normal tissues
- Guides monitoring for specific adverse events
- Not predictive of all toxicity (Fc-mediated mechanisms not captured)
6. Regulatory Guidance Reference
6a. FDA Guidances
| Document | Applicability | Key ADC-Specific Content |
|---|---|---|
| FDA Guidance: Clinical Pharmacology Considerations for Antibody-Drug Conjugates (Draft, 2022) | Primary ADC-specific clinical pharm guidance | Analyte selection, E-R recommendations, dose optimization, special populations |
| FDA Guidance: Population PK (2019) | popPK methodology | Model building, covariate selection, VPC requirements for submissions |
| FDA Guidance: Estimating the Maximum Safe Starting Dose (2005) | FIH starting dose | NOAEL → HED conversion, safety factor selection |
| FDA Guidance: M3(R2) (2010) | Non-clinical studies for IND | Study timing, GLP requirements |
| FDA Guidance: S9 Nonclinical Evaluation for Anticancer Pharmaceuticals (2010) | Oncology NCI framework | Abbreviated tox for advanced cancer; reproductive tox timing |
| FDA BioA Guidance (2018) | BMV for all methods | LLOQ, accuracy, precision, stability — full validation |
| FDA Guidance: Immunogenicity Assessment for Therapeutic Protein Products (2019) | ADA testing | Tiered testing, drug tolerance, clinical impact assessment |
6b. EMA Guidances
| Document | Applicability |
|---|---|
| EMA Guideline on the Clinical Investigation of the Pharmacokinetics of Therapeutic Proteins (2007) | Antibody PK design; TAb/cAb analyte selection |
| EMA Reflection Paper on ADCs (Draft, 2020; Final expected 2024–25) | ADC-specific: analyte panel, linker stability studies, immunogenicity |
| EMA Guideline on BMV (2012 + ICH M10 implementation) | Method validation harmonized with ICH M10 |
| CHMP SWP/28367/07 — MABEL Guidance (2007) | FIH starting dose using MABEL for high-risk biologics |
| EMA ICH S9 Implementation (EMA/CHMP/ICH/646107/2008) | Non-clinical for oncology |
6c. ICH Guidances
| ICH Document | Topic |
|---|---|
| ICH M10 (2022, Step 4) | Bioanalytical Method Validation — harmonized global standard; LBA chapter |
| ICH M3(R2) (2009) | Nonclinical Safety Studies Timing |
| ICH S9 (2009) | Nonclinical Evaluation for Anticancer Drugs |
| ICH E8(R1) (2021) | General Considerations for Clinical Studies |
| ICH E9(R1) (2019) | Statistical Principles; estimands framework (relevant for OS/PFS endpoints) |
| ICH S6(R1) (2011) | Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals |
| ICH Q8/Q9/Q10 | Pharmaceutical development, risk management, quality systems (manufacturing/CMC) |
6d. Industry Consensus White Papers (Referenced in Regulatory Submissions)
| Document | Content |
|---|---|
| Kaur et al. (2013) AAPS J (AAPS ADC Working Group) | Recommended analytes, assay platforms, validation approach |
| Gorovits et al. (2013) Bioanalysis | Hybrid LBA-LC/MS/MS methodology consensus |
| AAPS/FDA ADC Bioanalysis Workshop Report (2020) | Updated consensus: free payload stability, hybrid assay formats |
| Shankar et al. (2014) AAPS J | ADA testing strategy for ADCs — tiered algorithm |
| EBF (European Bioanalysis Forum) ADC recommendations (2019, 2022) | European bioanalytical practice consensus |
Key Papers
- Gibiansky & Gibiansky (2014) J Pharmacokinet Pharmacodyn — ADC PK/PD modeling framework
- Singh & Shah (2017) Drug Metab Dispos — allometric scaling of mAb PK
- Scheuher et al. (2022) Clin Pharmacol Ther — T-DXd exposure-response in DESTINY-Breast trials
- Friberg et al. (2002) J Clin Oncol — transit model for myelosuppression
- FDA Clinical Pharmacology Considerations for ADCs (Draft Guidance, 2022)