Physiologically based pharmacokinetic-quantitative systems toxicology and safety (PBPK-QSTS) modeling approach applied to predict the variability of amitriptyline pharmacokinetics and cardiac safety in populations and in individuals

Abstract

The physiologically based pharmacokinetic (PBPK) models allow for predictive assessment of variability in population of interest. One of the future application of PBPK modeling is in the field of precision dosing and personalized medicine. The aim of the study was to develop PBPK model for amitriptyline given orally, predict the variability of cardiac concentrations of amitriptyline and its main metabolite—nortriptyline in populations as well as individuals, and simulate the influence of those xenobiotics in therapeutic and supratherapeutic concentrations on human electrophysiology. The cardiac effect with regard to QT and RR interval lengths was assessed. The Emax model to describe the relationship between amitriptyline concentration and heart rate (RR) length was proposed. The developed PBPK model was used to mimic 29 clinical trials and 19 cases of amitriptyline intoxication. Three clinical trials and 18 cases were simulated with the use of PBPK-QSTS approach, confirming lack of cardiotoxic effect of amitriptyline in therapeutic doses and the increase in heart rate along with potential for arrhythmia development in case of amitriptyline overdose. The results of our study support the validity and feasibility of the PBPK-QSTS modeling development for personalized medicine.Electronic supplementary materialThe online version of this article (10.1007/s10928-018-9597-6) contains supplementary material, which is available to authorized users.

Virtual Twins: Understanding the Data Required for Model‐Informed Precision Dosing

“…To date, VTs have been generated by individualizing a limited number of systems parameters in established PBPK platforms. [1][2][3][4] Which data are required routinely, how these data are generated and stored, how models are best individualized and updated, and whether VTs can be deployed clinically for accurate dosing decisions, are all areas of uncertainties that require addressing (e.g., prediction of complex drug-drug-genedisease interactions). Much data for VTs is readily available but underutilized, and validated biomarkers of ADME processes from "liquid biopsies" are currently driving superior model individualization.…”

“…It is proposed that step 1 (“stratification”), step 2 (“personalization”), and step 3 (“optimization”) are all required to shift adequately the prediction of PK from the population level to the individual. Fixed parameters in the VT publications to date include age, gender, kidney function, and DME genotype and/or phenotype . Monte Carlo simulation then allows sensible variability in PK for the VTs to be estimated from covariates of the “nonfixed” default system parameters, i.e., similar to population level PBPK M&S. Figure illustrates this workflow using the example of a patient starting olanzapine for schizophrenia …”

“…Fixed parameters in the VT publications to date include age, gender, kidney function, and DME genotype and/ or phenotype. [1][2][3][4] Monte Carlo simulation then allows sensible variability in PK for the VTs to be estimated from covariates of the "nonfixed" default system parameters, i.e., similar to population level PBPK M&S. Figure 2 illustrates this workflow using the example of a patient starting olanzapine for schizophrenia. 1…”

“…Interest in VTs is for several reasons: (i) Improving predictability of PBPK for stratified dosing of the “special populations”; (ii) evolving workflows to “reverse translate” biomarker data from clinical studies to improve models (“middle‐out”); (iii) recent proof of concept work, including the prediction of olanzapine trough concentrations in patients, cardiotoxicity of citalopram and amitriptyline using PBPK‐QSP, and morphine concentrations in critically ill neonates; (iv) potential to predict doses prior to commencing drug treatment, and with or without biomarker feedback during chronic therapy; and (v) increasing expectations by patients and payers for “personalized medicine.”…”

“…The approach has four published reports to date, two of which have pharmacodynamic predictions via linked quantitative systems pharmacology (QSP) models. [1][2][3][4] Importantly, VTs are patient focused rather than drug focused. Predictions of drug behavior depend on the interactions of certain elements of VTs pertinent to a specific drug (e.g., eliminating pathways), some of which may or may not be relevant to the other drugs taken.…”