The Third Dimension of Pharmacokinetic/ Pharmacodynamic Theory: Adaptive Rate Capacity as a Conserved Constraint on Biological Tolerability
Kleinbloesem, C. H.; Braal, C. L.
Show abstract
Background Classical pharmacokinetic-pharmacodynamic (PK/PD) theory models exposure-effect in two dimensions: magnitude and time. Rate-dependent toxicity has been documented across therapeutic domains but never formalised as a conserved biological constraint. Methods We developed the Human Adaptive Rate Limit (HARL) framework, formalising the maximum tolerable velocity as |dS/dt|_max = sigma_max / tau. We validated HARL across five domains using published trial data and a reanalysis of the longitudinal biomarker data from the 202-patient CAR-T cohort of Wei et al (2023). An 8-ODE quantitative systems pharmacology model guided biomarker selection. Early biomarker velocities (maximum positive slope within days 0-5) were computed for ferritin and D-dimer. Patients were classified as high-risk only if both velocities exceeded their thresholds (dual-velocity classifier). Thresholds were identified by grid-search optimisation of the Youden index and assessed by leave-one-out cross-validation. Findings A prospective crossover study (Kleinbloesem 1987, n=8) demonstrated that matched steady-state nifedipine concentrations produce divergent haemodynamic responses depending solely on rate of rise, anticipating the dose-related mortality signal subsequently reported across ~8350 patients with coronary heart disease (Furberg 1995), a meta-analysis that was itself debated. Convergent evidence spans haematology (CHOIR, 1432 patients, hazard ratio [HR] 1.34 [1.03-1.74] for aggressive Hb correction), radiation (dose-rate effectiveness factor [DDREF] 1.5-2.0), and infusion pharmacology. In the CAR-T cohort, high-risk classification (ferritin >232 ng/mL per day AND D-dimer >1.21 mg/L per day) predicted severe CRS with 100% sensitivity (~78% specificity) in safety rule-out mode and 91.1% sensitivity (93.6% specificity, AUC 0.95 [95% CI 0.91-0.98]) in Youden-optimised mode. Median kinetic lead time was 4 days (range 3-7) before clinical decompensation. Interpretation Biological tolerability is three-dimensional. HARL unifies rate-dependent toxicity across domains spanning minutes to weeks. MTDyn--specifying target level and allowable rate of change--should supplement conventional dose-response assessment.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Pharmacometabolomics Identifies Candidate Predictor Metabolites of an L-carnitine Treatment Mortality Benefit in Septic Shock 90%
- A Framework to Quantify Disparities in Pharmacogenomic Treatment Concordance and Drug Response Outcomes 90%
- A randomized, controlled, feasibility study of RD-X19 in patients with mild-to-moderate COVID-19 in the outpatient setting 89%
Similar papers in this journal
- Prioritisation of potential anti-SARS-CoV-2 drug repurposing opportunities based on ability to achieve adequate target site concentrations derived from their established human pharmacokinetics 91%
- DrugWAS: Leveraging drug-wide association studies to facilitate drug repurposing for COVID-19 90%
- Model-informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease 90%
Similar papers in this journal
- A Standardized Metric to Enhance Clinical Trial Design and Outcome Interpretation in Type 1 Diabetes 93%
- Angiogenic and Immune Predictors of Neoadjuvant Axitinib Response in Renal Cell Carcinoma with Venous Tumour Thrombus 93%
- White Blood Cell and Platelet Dynamics Define Human Inflammatory Recovery 92%
Similar papers in this journal
- A Precision Gene Engineered B Cell Medicine Producing Sustained Levels of Active Factor IX for Hemophilia B Therapy 94%
- Pre-conditioning Modifies the Tumor Microenvironment to Enhance Solid Tumor CAR T Cell Efficacy and Endogenous Immunity 92%
- Efficient multiplex non-viral engineering and expansion of polyclonal γδ CAR-T cells for immunotherapy 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.