Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations

The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

[1]  Sudhakar M. Pai,et al.  Pharmacodynamic parameter estimation: Population size versus number of samples , 2005, The AAPS Journal.

[2]  Andrew C. Hooker,et al.  Adaptive Optimal Designs for Dose-Finding Studies with Time-to-Event Outcomes , 2017, The AAPS Journal.

[3]  J. Lehár,et al.  Multi-target therapeutics: when the whole is greater than the sum of the parts. , 2007, Drug discovery today.

[4]  Eric A. Strömberg,et al.  The effect of using a robust optimality criterion in model based adaptive optimization , 2017, Journal of Pharmacokinetics and Pharmacodynamics.

[5]  Ryan P. Million,et al.  Tapping the potential of fixed-dose combinations , 2007, Nature Reviews Drug Discovery.

[6]  W F Ebling,et al.  Is it possible to estimate the parameters of the sigmoid Emax model with truncated data typical of clinical studies? , 1996, Journal of pharmaceutical sciences.

[7]  Yaning Wang,et al.  Evaluation of false positive rate based on exposure–response analyses for two compounds in fixed-dose combination products , 2011, Journal of Pharmacokinetics and Pharmacodynamics.

[8]  Holger Dette,et al.  Optimal Designs for Estimating the Interesting Part of a Dose-Effect Curve , 2007, Journal of biopharmaceutical statistics.

[9]  William J. Jusko,et al.  Assessment of non-linear combination effect terms for drug–drug interactions , 2016, Journal of Pharmacokinetics and Pharmacodynamics.

[10]  C. I. Bliss THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[11]  J R Powell,et al.  Pharmacometrics at FDA: Evolution and Impact on Decisions , 2007, Clinical pharmacology and therapeutics.

[12]  Yaning Wang,et al.  Impact of Pharmacometric Analyses on New Drug Approval and Labelling Decisions , 2011, Clinical pharmacokinetics.

[13]  M. Krams,et al.  Innovative Approaches for Designing and Analyzing Adaptive Dose-Ranging Trials , 2007, Journal of biopharmaceutical statistics.

[14]  Andrew C. Hooker,et al.  Improved precision of exposure–response relationships by optimal dose-selection. Examples from studies of receptor occupancy using PET and dose finding for neuropathic pain treatment , 2015, Journal of Pharmacokinetics and Pharmacodynamics.

[15]  N Holford,et al.  Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4–5 December 2014) , 2017, CPT: pharmacometrics & systems pharmacology.

[16]  L. Kux OF HEALTH AND HUMAN SERVICES Food and Drug Administration , 2014 .

[17]  Cedric E. Ginestet ggplot2: Elegant Graphics for Data Analysis , 2011 .

[18]  J. Woodcock,et al.  Development of novel combination therapies. , 2011, The New England journal of medicine.

[19]  Hadley Wickham,et al.  The Split-Apply-Combine Strategy for Data Analysis , 2011 .

[20]  Yasunori Aoki,et al.  Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection , 2017, Journal of Pharmacokinetics and Pharmacodynamics.

[21]  Sudhakar M. Pai,et al.  Population Pharmacodynamic Parameter Estimation from Sparse Sampling: Effect of Sigmoidicity on Parameter Estimates , 2009, The AAPS Journal.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  Elizabeth Stewart,et al.  BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action , 2016, Scientific Reports.

[24]  Theodoros Papathanasiou,et al.  Quantification of the Pharmacodynamic Interaction of Morphine and Gabapentin Using a Response Surface Approach , 2017, The AAPS Journal.

[25]  Nori Geary,et al.  Understanding synergy. , 2013, American journal of physiology. Endocrinology and metabolism.

[26]  W. Greco,et al.  The search for synergy: a critical review from a response surface perspective. , 1995, Pharmacological reviews.

[27]  Theodoros Papathanasiou,et al.  Co-administration of morphine and gabapentin leads to dose dependent synergistic effects in a rat model of postoperative pain. , 2016, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[28]  S L Shafer,et al.  Response Surface Model for Anesthetic Drug Interactions , 2000, Anesthesiology.

[29]  J Jack Lee,et al.  A Generalized Response Surface Model with Varying Relative Potency for Assessing Drug Interaction , 2006, Biometrics.

[30]  Ting-Chao Chou,et al.  Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies , 2006, Pharmacological Reviews.

[31]  C. Tornøe,et al.  Establishing Good Practices for Exposure–Response Analysis of Clinical Endpoints in Drug Development , 2015, CPT: pharmacometrics & systems pharmacology.