Nikunjkumar Patel

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Quantitative prediction of formulation-specific food effects and their population variability from in vitro data with the physiologically-based ADAM model: A case study using the BCS/BDDCS Class II drug nifedipine

Quantitative prediction of food effects (FE) upon drug pharmacokinetics, including population variability, in advance of human trials may help with trial design by optimising the number of subjects and sampling times when a clinical study is warranted or by negating the need for conduct of clinical studies. Classification and rule-based systems such as the BCS and BDDCS and statistical QSARs are widely used to anticipate the nature of FE in early drug development. However, their qualitative rather than quantitative nature makes them less appropriate for assessing the magnitude of FE. Moreover, these approaches are based upon drug properties alone and are not appropriate for estimating potential formulation-specific FE on modified or controlled release products. In contrast, physiologically-based mechanistic models can consider the scope and interplay of a range of physiological changes after food intake and, in combination with appropriate in vitro drug- and formulation-specific data, can make quantitative predictions of formulation-specific FE including the inter-individual variability of such effects. Herein the Advanced Dissolution, Absorption and Metabolism (ADAM) model is applied to the prediction of formulation-specific FE for BCS/BDDCS Class II drug and CYP3A4 substrate nifedipine using as far as possible only in vitro data. Predicted plasma concentration profiles of all three studied formulations under fasted and fed states are within 2-fold of clinically observed profiles. The % prediction error (%PE) in fed-to-fasted ratio of Cmax and AUC were less than 5% for all formulations except for the Cmax of Nifedicron (%PE=-29.6%). This successful case study should help to improve confidence in the use of mechanistic physiologically-based models coupled with in vitro data for the anticipation of FE in advance of in vivo studies. However, it is acknowledged that further studies with drugs/formulations exhibiting a wide range of properties are required to further validate this methodology.

Assessment of Bioequivalence of Weak Base Formulations Under Various Dosing Conditions Using Physiologically Based Pharmacokinetic Simulations in Virtual Populations. Case Examples: Ketoconazole and Posaconazole

Postabsorptive factors which can affect systemic drug exposure are assumed to be dependent on the active pharmaceutical ingredient (API), and thus independent of formulation. In contrast, preabsorptive factors, for example, hypochlorhydria, might affect systemic exposure in both an API and a formulation-dependent way. The aim of this study was to evaluate whether the oral absorption of 2 poorly soluble, weakly basic APIs, ketoconazole (KETO) and posaconazole (POSA), would be equally sensitive to changes in dissolution rate under the following dosing conditions-coadministration with water, with food, with carbonated drinks, and in drug-induced hypochlorhydria. The systems-components of validated absorption and PBPK models for KETO and POSA were modified to simulate the above-mentioned clinical scenarios. Virtual bioequivalence studies were then carried out to investigate whether formulation effects on the plasma profile vary with the dosing conditions. The slow precipitation of KETO upon reaching the upper part of the small intestine renders its absorption more sensitive to the completeness of gastric dissolution and thus to the gastric environment than POSA, which is subject to extensive precipitation in response to a pH shift. The virtual bioequivalence studies showed that hypothetical test and reference formulations containing KETO would be bioequivalent only if the microenvironment in the stomach enables complete gastric dissolution. We conclude that physiologically based pharmacokinetic modeling and simulation has excellent potential to address issues close to bedside such as optimizing dosing conditions. By studying virtual populations adapted to various clinical situations, clinical strategies to reduce therapeutic failures can be identified.

Early assessment of proarrhythmic risk of drugs using the in vitro data and single-cell-based in silico models: proof of concept

Abstract Background and purpose: To determine the predictive performance of in silico models using drug-specific preclinical cardiac electrophysiology data to investigate drug-induced arrhythmia risk (e.g. Torsade de pointes (TdP)) in virtual human subjects. Experimental approach: To assess drug proarrhythmic risk, we used a set of in vitro electrophysiological measurements describing ion channel inhibition triggered by the investigated drugs. The Cardiac Safety Simulator version 2.0 (CSS; Simcyp, Sheffield, UK) platform was used to simulate human left ventricular cardiac myocyte action potential models. Results: This study shows the impact of drug concentration changes on particular ionic currents by using available experimental data. The simulation results display safety threshold according to drug concentration threshold and log (threshold concentration/ effective therapeutic plasma concentration (ETPC)). Conclusion and implications: We reproduced the underlying biophysical characteristics of cardiac cells resulted in effects of drugs associated with cardiac arrhythmias (action potential duration (APD) and QT prolongation and TdP) which were observed in published 3D simulations, yet with much less computational burden.

Virtual bioequivalence for achlorhydric subjects: The use of PBPK modelling to assess the formulation-dependent effect of achlorhydria

&NA; Majority of bioequivalence studies are conducted in healthy volunteers. It has been argued that bioequivalence may not necessarily hold true in relevant patient populations due to a variety of reasons which affect one formulation more than the other for instance in achlorhydric patients where elevated gastric pH may lead to differential effects on formulations which are pH‐sensitive with respect to release or dissolution. We therefore examined achlorhydria‐related disparity in bioequivalence of levothyroxine and nifedipine formulations using virtual bioequivalence within a physiologically‐based pharmacokinetic (PBPK) modelling framework. The in vitro dissolution profiles at neutral pH were incorporated into PBPK models to mimic the achlorhydria with in vitro–in vivo relationship established using bio‐relevant pH media. The PBPK models successfully reproduced the outcome of the bioequivalence studies in healthy volunteers under the normal conditions as well as under proton pump inhibitor‐induced achlorhydria. The geometric mean test/reference ratios for Cmax and AUC between levothyroxine tablet and capsule in patients receiving proton pump inhibitor were 1.21 (90%CI, 1.13–1.29) and 1.09 (90%CI, 1.02–1.17), respectively. Extension of the virtual bioequivalence study to Japanese elderly, who show high incidence of achlorhydria, indicated bio‐inequivalence which Cmax and AUC ratios between nifedipine control‐released reference and test formulations were 3.08 (90%CI, 2.81–3.38) and 1.57 (90%CI, 1.43–1.74), respectively. Virtual bioequivalence studies through the PBPK models can highlight the need for conduct of specific studies in elderly Japanese populations where there are discrepancies in pH‐sensitivity of dissolution between the test and reference formulations. Graphical abstract Figure. No caption available.

Examining the Use of a Mechanistic Model to Generate an In Vivo/In Vitro Correlation: Journey Through a Thought Process

The attention and interest in establishing in vivo/in vitro correlations (IVIVCs) is grounded in its tremendous utility as a prognostic tool. It can be used to support formulation optimization, predict in vivo drug exposure across a potential patient population, select a biologically relevant in vitro dissolution test condition, and support the use of in vitro dissolution data as a surrogate for in vivo bioequivalence trials. The pharmacological and statistical implications of this correlation are linked to the method by which the IVIVC was determined and to the assumptions and optimization approaches integrated into the estimation procedure. Using previously published data generated in normal healthy volunteers, an IVIVC for metoprolol was established using a mechanistic modeling approach. Within that framework, we explored the consequences of (1) our method of fitting a single Weibull function to the in vivo dissolution, (2) our selection of weighting scheme and optimization approaches, (3) the impact of applying a fixed versus fitted gastric emptying time, and 4) the importance of factoring population variability into our IVIVC estimation and profile reconvolution. We identified those factors found to be critical in terms of their influence on the accuracy of our predicted systemic metoprolol concentration-time profiles. We considered the strengths and weaknesses of our approach and discussed how the results of this study may impact efforts to generate IVIVCs with compounds presenting physicochemical characteristics different from that of metoprolol.

In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation, and Precipitation of Posaconazole

The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole, which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately 2-fold higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semimechanistic modeling of a two-stage medium change in vitro dissolution test. The 2-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a 2-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation, and precipitation for a weakly basic compound in part informed by modeling of in vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage medium change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost identical CSR to the clinical study value; this had no significant impact on the PBPK model predictions.

Model-Based Analysis of Biopharmaceutic Experiments To Improve Mechanistic Oral Absorption Modeling: An Integrated in Vitro in Vivo Extrapolation Perspective Using Ketoconazole as a Model Drug

Mechanistic modeling of in vitro data generated from metabolic enzyme systems (viz., liver microsomes, hepatocytes, rCYP enzymes, etc.) facilitates in vitro-in vivo extrapolation (IVIV_E) of metabolic clearance which plays a key role in the successful prediction of clearance in vivo within physiologically-based pharmacokinetic (PBPK) modeling. A similar concept can be applied to solubility and dissolution experiments whereby mechanistic modeling can be used to estimate intrinsic parameters required for mechanistic oral absorption simulation in vivo. However, this approach has not widely been applied within an integrated workflow. We present a stepwise modeling approach where relevant biopharmaceutics parameters for ketoconazole (KTZ) are determined and/or confirmed from the modeling of in vitro experiments before being directly used within a PBPK model. Modeling was applied to various in vitro experiments, namely: (a) aqueous solubility profiles to determine intrinsic solubility, salt limiting solubility factors and to verify pKa; (b) biorelevant solubility measurements to estimate bile-micelle partition coefficients; (c) fasted state simulated gastric fluid (FaSSGF) dissolution for formulation disintegration profiling; and (d) transfer experiments to estimate supersaturation and precipitation parameters. These parameters were then used within a PBPK model to predict the dissolved and total (i.e., including the precipitated fraction) concentrations of KTZ in the duodenum of a virtual population and compared against observed clinical data. The developed model well characterized the intraluminal dissolution, supersaturation, and precipitation behavior of KTZ. The mean simulated AUC0-t of the total and dissolved concentrations of KTZ were comparable to (within 2-fold of) the corresponding observed profile. Moreover, the developed PBPK model of KTZ successfully described the impact of supersaturation and precipitation on the systemic plasma concentration profiles of KTZ for 200, 300, and 400 mg doses. These results demonstrate that IVIV_E applied to biopharmaceutical experiments can be used to understand and build confidence in the quality of the input parameters and mechanistic models used for mechanistic oral absorption simulations in vivo, thereby improving the prediction performance of PBPK models. Moreover, this approach can inform the selection and design of in vitro experiments, potentially eliminating redundant experiments and thus helping to reduce the cost and time of drug product development.

Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development

Cardiotoxicity is among the top drug safety concerns, and is of specific interest in tuberculosis, where this is a known or potential adverse event of current and emerging treatment regimens. As there is a need for a tool, beyond the QT interval, to quantify cardiotoxicity early in drug development, an empirical decision tree based classifier was developed to predict the risk of Torsades de pointes (TdP). The cardiac risk algorithm was developed using pseudo-electrocardiogram (ECG) outputs derived from cardiac myocyte electromechanical model simulations of increasing concentrations of 96 reference compounds which represented a range of clinical TdP risk. The algorithm correctly classified 89% of reference compounds with moderate sensitivity and high specificity (71 and 96%, respectively) as well as 10 out of 12 external validation compounds and the anti-TB drugs moxifloxacin and bedaquiline. The cardiac risk algorithm is suitable to help inform early drug development decisions in TB and will evolve with the addition of emerging data.