Evidence map›Paper›PMID 42298775›Full record

ArticlePharmacology research & perspectives2026

Differential Impact of Pharmacokinetic and Pharmacodynamic Variability on Response to Combination Therapy.

Kuteesa R Bisaso, Ronald Kadada Karyaburo, Jackson K Mukonzo, Ene I Ette

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Article in Pharmacology research & perspectives, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kuteesa R BisasoBreakthrough Analytics Limited, Kampala, Uganda.ORCID https://orcid.org/0000-0001-7721-4005
Ronald Kadada KaryaburoBreakthrough Analytics Limited, Kampala, Uganda.
Jackson K MukonzoDepartment of Pharmacology, Makerere University College of Health Sciences, Kampala, Uganda.ORCID https://orcid.org/0000-0002-0573-1908
Ene I EtteAnoixis Corporation, Natick, Massachusetts, USA.ORCID https://orcid.org/0000-0003-3227-0454

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interindividual variability (IIV) in drug response complicates both fixed-dose design and individualized therapy. Understanding how pharmacokinetic (PK) and pharmacodynamic (PD) processes jointly shape this variability is essential, particularly in combination therapy, where multiple interacting pathways influence outcomes. This study employed a unified PK-Signal-Reaction-Stimulus-Response (PK-SRSR) modeling framework to quantify and compare the impact of IIV in PK and PD parameters under monotherapy and combination therapy. Monte Carlo simulations were used to generate response distributions across hypothetical physiological conditions, while Sobol variance-based sensitivity analysis decomposed total response variability into contributions from individual parameters (FOI) and their interactions (TOI). Results showed that PD variability, especially that related to system-level modulatory processes (S0A, S0B, βA, βB, and βiA), exerted a greater influence on overall response than drug effect level PD (SmaxA, SmaxB, KA, and KB) variability and PK (CLA/F and CLB/F) variability. Variability in response was most sensitive to S0B, βB (FOI > 34%, TOI > 32%) during monotherapy and SmaxA (FOI > 25%, TOI > 27%), SmaxB (FOI > 17%, TOI > 24%), and βB (FOI > 32%, TOI > 34%) during combination therapy. At 45% CV in all parameters, variability in response was lower during combination therapy (CV = 44%, 42%, and 33%) than during monotherapy (CV = 59%, 52%, and 53%) for additive, antagonistic, and synergistic PD interactions, respectively. Introducing a second drug targeting a modulatory pathway reduced response variability by stabilizing regulatory mechanisms, explaining previous observations in which patients achieve therapeutic response despite suboptimal plasma concentrations during combination therapy. Coordinated dose optimization of all agents, supported by response-guided, system-aware strategies, may yield more robust, consistent, and individualized treatment outcomes than traditional PK-guided dosing approaches.

Indexed as

Models, BiologicalComputer SimulationDose-Response Relationship, DrugDrug Therapy, CombinationHumansMonte Carlo MethodTreatment Effect Heterogeneitycombination therapyinterindividual variabilitymonte Carlo simulationpharmacodynamicpharmacodynamic drug–drug interactionspharmacokineticresponse variabilitysignal‐reaction–stimulus–response modelsobol sensitivity analysissystem‐level variability

Identifiers

PMID42298775
PMCPMC13269674

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.