Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE: Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications. METHODS: Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs. RESULTS: Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications. CONCLUSIONS: The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Clopidogrel and Its Four Relevant Metabolites for CYP2B6, CYP2C8, CYP2C19, and CYP3A4 Drug-Drug-Gene Interaction Predictions.

The antiplatelet agent clopidogrel is listed by the FDA as a strong clinical index inhibitor of cytochrome P450 (CYP) 2C8 and weak clinical inhibitor of CYP2B6. Moreover, clopidogrel is a substrate of-among others-CYP2C19 and CYP3A4. This work presents the development of a whole-body physiologically based pharmacokinetic (PBPK) model of clopidogrel including the relevant metabolites, clopidogrel carboxylic acid, clopidogrel acyl glucuronide, 2-oxo-clopidogrel, and the active thiol metabolite, with subsequent application for drug-gene interaction (DGI) and drug-drug interaction (DDI) predictions. Model building was performed in PK-Sim® using 66 plasma concentration-time profiles of clopidogrel and its metabolites. The comprehensive parent-metabolite model covers biotransformation via carboxylesterase (CES) 1, CES2, CYP2C19, CYP3A4, and uridine 5'-diphospho-glucuronosyltransferase 2B7. Moreover, CYP2C19 was incorporated for normal, intermediate, and poor metabolizer phenotypes. Good predictive performance of the model was demonstrated for the DGI involving CYP2C19, with 17/19 predicted DGI AUClast and 19/19 predicted DGI Cmax ratios within 2-fold of their observed values. Furthermore, DDIs involving bupropion, omeprazole, montelukast, pioglitazone, repaglinide, and rifampicin showed 13/13 predicted DDI AUClast and 13/13 predicted DDI Cmax ratios within 2-fold of their observed ratios. After publication, the model will be made publicly accessible in the Open Systems Pharmacology repository.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions.

PURPOSE: To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs). METHODS: The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002-80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model. RESULTS: The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values. CONCLUSIONS: A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

A quantitative systems pharmacology model for acute viral hepatitis B.

Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80-90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B. In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients.

BACKGROUND AND OBJECTIVES: The thrombin inhibitor dabigatran is administered as the prodrug dabigatran etexilate, which is a substrate of esterases and P-glycoprotein (P-gp). Dabigatran is eliminated via renal excretion but is also a substrate of uridine 5'-diphospho (UDP)-glucuronosyltransferases (UGTs). The objective of this study was to build a physiologically based pharmacokinetic (PBPK) model comprising dabigatran etexilate, dabigatran, and dabigatran 1-O-acylglucuronide to describe the pharmacokinetics in healthy adults and renally impaired patients mechanistically. METHODS: Model development and evaluation were carried out using (i) physicochemical and absorption, distribution, metabolism, and excretion (ADME) parameter values of all three analytes; (ii) concentration-time profiles from 13 studies of healthy and renally impaired individuals after varying doses (0.1-300 mg), intravenous (dabigatran) and oral (dabigatran etexilate) administration, and different formulations of dabigatran etexilate (capsule, solution); and (iii) drug-drug interaction studies of dabigatran with the P-gp perpetrators rifampin (inducer) and clarithromycin (inhibitor). RESULTS: A PBPK model of dabigatran was successfully developed. The predicted area under the plasma concentration-time curve, trough concentration, and half-life values of the assessed clinical studies satisfied the two-fold acceptance criterion. Metabolic clearances of dabigatran etexilate and dabigatran were implemented using data on carboxylesterase 1/2 enzymes and UGT subtype 2B15. In severe renal impairment, the UGT2B15 metabolism and the P-gp transport in the model were reduced to 67% and 65% of the rates in healthy adults. CONCLUSION: This is the first implementation of a PBPK model for dabigatran to distinguish between the prodrug, active moiety, and main active metabolite. Following adjustment of the UGT2B15 metabolism and P-gp transport rates, the PBPK model accurately predicts the pharmacokinetics in renally impaired patients.

A systems pharmacology model for inflammatory bowel disease.

MOTIVATION: The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. RESULTS: In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.

Advanced Boolean modeling of biological networks applied to systems pharmacology.

Motivation: Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results: In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets. Availability and Implementation: The source code is available at https://github.com/SPIDDOR/SPIDDOR . Contact: itzirurzun@alumni.unav.es , itroconiz@unav.es. Supplementary information: Supplementary data are available at Bioinformatics online.

Towards patient stratification and treatment in the autoimmune disease lupus erythematosus using a systems pharmacology approach.

Drug development in Systemic Lupus Erythematosus (SLE) has been hindered by poor translation from successful preclinical experiments to clinical efficacy. This lack of success has been attributed to the high heterogeneity of SLE patients and to the lack of understanding of disease physiopathology. Modelling approaches could be useful for supporting the identification of targets, biomarkers and patient subpopulations with differential response to drugs. However, the use of traditional quantitative models based on differential equations is not justifiable in a sparse data situation. Boolean networks models are less demanding on the required data to be implemented and can provide insights into the dynamics of biological networks. This methodology allows the integration of all the available knowledge into a single framework to evaluate the behavior of the system under different conditions and test hypotheses about unknown aspects of the disease. In this proof-of-concept study, we explored the potential of a systems pharmacology model based on Boolean networks to support drug development in SLE. We focused the analysis on the antigen presentation by the antigen presenting cells (APC) to the T-cells to evaluate the scope of this methodology in a medium size network before full implementation of the whole SLE pathway. The heterogeneity of SLE patients was replicated using this methodology simulating subjects with distinct pathway alterations. A perturbation analysis of the network coupled with clustering analysis showed potential to identify drug targets, optimal combinatorial regimens and subpopulations of responders and non-responders to drug treatment. We propose this approach as a first step towards the development of more quantitative platforms to address the current challenges in drug development for complex diseases.

Review on modeling anti-antibody responses to monoclonal antibodies.

Monoclonal antibodies (mAbs) represent a therapeutic strategy that has been increasingly used in different diseases. mAbs are highly specific for their targets leading to induce specific effector functions. Despite their therapeutic benefits, the presence of immunogenic reactions is of growing concern. The immunogenicity identified as anti-drug antibodies (ADA) production due to the continuous administration of mAbs may affect the pharmacokinetics (PK) and/or the pharmacodynamics (PD) of mAbs administered to patients. Therefore, the immunogenicity and its clinical impact have been studied by several authors using PK modeling approaches. In this review, the authors try to present all those models under a unique theoretical mechanism-based framework incorporating the main considerations related to ADA formation, and how ADA may affect the efficacy or toxicity profile of some therapeutic biomolecules.

Permutation Test (PT) and Tolerated Difference Test (TDT): two new, robust and powerful nonparametric tests for statistical comparison of dissolution profiles.

The most popular way of comparing oral solid forms of drug formulations from different batches or manufacturers is through dissolution profile comparison. Usually, a similarity factor known as (f2) is employed; However, the level of confidence associated with this method is uncertain and its statistical power is low. In addition, f2 lacks the flexibility needed to perform in special scenarios. In this study two new statistical tests based on nonparametrical Permutation Test theory are described, the Permutation Test (PT), which is very restrictive to confer similarity, and the Tolerated Difference Test (TDT), which has flexible restrictedness to confer similarity, are described and compared to f2. The statistical power and robustness of the tests were analyzed by simulation using the Higuchi, Korsmayer, Peppas and Weibull dissolution models. Several batches of oral solid forms were simulated while varying the velocity of dissolution (from 30 min to 300 min to dissolve 85% of the total content) and the variability within each batch (CV 2-30%). For levels of variability below 10% the new tests exhibited better statistical power than f2 and equal or better robustness than f2. TDT can also be modified to distinguish different levels of similarity and can be employed to obtain customized comparisons for specific drugs. In conclusion, two new methods, more versatile and with a stronger statistical basis than f2, are described and proposed as viable alternatives to that method. Additionally, an optimized time sampling strategy and an experimental design-driven strategy for performing dissolution profile comparisons are described.