Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

A clinical population pharmacokinetic/pharmacodynamic model for BIIB059, a monoclonal antibody for the treatment of systemic and cutaneous lupus erythematosus.

A population pharmacokinetic/pharmacodynamic (popPK/PD) model for BIIB059 (anti-blood dendritic cell antigen 2 [anti-BDCA2]), a humanized immunoglobulin G1 monoclonal antibody currently under development for the treatment of SLE and CLE, is presented. BIIB059 binds BDCA2, a plasmacytoid dendritic cell (pDC)-specific receptor that inhibits the production of IFN-I and other inflammatory mediators when ligated. Phase 1 PK and PD data of healthy adult volunteers (HV, n = 87) and SLE subjects (n = 22) were utilized for the development of the popPK/PD model. The data included single and multiple dosing of intravenous and subcutaneous BIIB059. BDCA2 internalization (PD marker) was measured for all subjects by monitoring reduction of BDCA2 on pDC cell surface and used for development of the popPD model. A two-compartment popPK model with linear plus non-linear elimination was found to best describe BIIB059 PK. BDCA2 levels were best captured using an indirect response model with stimulation of the elimination of BDCA2. Clearance in SLE subjects was 25% higher compared to HV (6.87 vs 5.52 mL/h). Bodyweight was identified as only other covariate on clearance and central volume. The estimates of EC50 and Emax were 0.35 µg/mL and 8.92, respectively. No difference in EC50 and Emax was observed between SLE and HV. The popPK/PD model described the data accurately, as evaluated by pcVPCs and bootstrap. The presented popPK/PD model for BIIB059 provides valuable insight into the dynamics and dose-response relationship of BIIB059 for the treatment of SLE and CLE and was used to guide dose selection for the Phase 2 clinical study (NCT02847598).

A pre-clinical quantitative model predicts the pharmacokinetics/pharmacodynamics of an anti-BDCA2 monoclonal antibody in humans.

BIIB059 is a novel humanized monoclonal antibody (mAb) that is currently under development for the treatment of Systemic Lupus Erythematosus and Cutaneous Lupus Erythematosus. BIIB059 is targeted against the blood dendritic cell antigen 2 (BDCA2), a receptor exclusively expressed on the surface of plasmacytoid dendritic cells (pDCs). Herein, we utilized pre-clinical pharmacokinetic (PK) and pharmacodynamic (PD) data to develop a non-human primate (NHP) model and to address whether the NHP model can be successfully scaled to predict the human PK/PD. In particular, PK data from 17 cynomolgus monkeys were utilized for PK model development, wherein BIIB059 was administered intravenously (1 and 10 mg/kg single-dosing and 5 mg/kg multiple-dosing) or subcutaneously (0.2 and 7.5 mg/kg single-dosing). Additionally, PD data (BDCA2 receptor density on pDCs) from 6 cynomolgus monkeys were used for the development of the PD model. The developed NHP two-compartment PK model, linked with an indirect response PD model, was subsequently scaled to humans by combining traditional allometric PK scaling with sensitivity-analysis-driven scaling of the PD. The scaled PK/PD model was then used to simulate the human PK/PD for different dose levels. When clinical data from the BIIB059 Phase I study became available, they were used to evaluate the predictability of the scaled PK/PD model and the model simulations were in agreement with the clinical data. Therefore, the presented approach is suggested to be employed in scaling pre-clinical mAb models to support the selection of safe first-in-human doses and, more broadly, the prediction of PK/PD in the clinic.

Preemptive Panel-Based Pharmacogenetic Testing: The Time is Now.

While recent discoveries have paved the way for the use of genotype-guided prescribing in some clinical environments, significant debate persists among clinicians and researchers about the optimal approach to pharmacogenetic testing in clinical practice. One crucial factor in this debate surrounds the timing and methodology of genotyping, specifically whether genotyping should be performed reactively for targeted genes when a single drug is prescribed, or preemptively using a panel-based approach prior to drug prescribing. While early clinical models that employed a preemptive approach were largely developed in academic health centers through multidisciplinary efforts, increasing examples of pharmacogenetic testing are emerging in community-based and primary care practice environments. However, educational and practice-based resources for these clinicians remain largely nonexistent. As such, there is a need for the health care system to shift its focus from debating about preemptive genotyping to developing and disseminating needed resources to equip frontline clinicians for clinical implementation of pharmacogenetics. Providing tools and guidance to support these emerging models of care will be essential to support the thoughtful, evidence-based use of pharmacogenetic information in diverse clinical practice environments. Specifically, the creation of efficient and accurate point-of-care resources, practice-based tools, and clinical models is needed, along with identification and dissemination of sustainable avenues for pharmacogenetic test reimbursement.

Clinical Practice Recommendations on Genetic Testing of CYP2C9 and VKORC1 Variants in Warfarin Therapy.

OBJECTIVE: To systematically review evidence on genetic variants influencing outcomes during warfarin therapy and provide practice recommendations addressing the key questions: (1) Should genetic testing be performed in patients with an indication for warfarin therapy to improve achievement of stable anticoagulation and reduce adverse effects? (2) Are there subgroups of patients who may benefit more from genetic testing compared with others? (3) How should patients with an indication for warfarin therapy be managed based on their genetic test results? METHODS: A systematic literature search was performed for VKORC1 and CYP2C9 and their association with warfarin therapy. Evidence was critically appraised, and clinical practice recommendations were developed based on expert group consensus. RESULTS: Testing of VKORC1 (-1639G>A), CYP2C9*2, and CYP2C9*3 should be considered for all patients, including pediatric patients, within the first 2 weeks of therapy or after a bleeding event. Testing for CYP2C9*5, *6, *8, or *11 and CYP4F2 (V433M) is currently not recommended. Testing should also be considered for all patients who are at increased risk of bleeding complications, who consistently show out-of-range international normalized ratios, or suffer adverse events while receiving warfarin. Genotyping results should be interpreted using a pharmacogenetic dosing algorithm to estimate the required dose. SIGNIFICANCE: This review provides the latest update on genetic markers for warfarin therapy, clinical practice recommendations as a basis for informed decision making regarding the use of genotype-guided dosing in patients with an indication for warfarin therapy, and identifies knowledge gaps to guide future research.

Clinical implementation of genetic testing in medicine: a US regulatory science perspective.

Heterogeneity of treatment effects in unselected patient populations has stimulated various strategic approaches to reduce variability and uncertainty and improve individualization of drug selection and dosing. The rapid growth of DNA sequencing and related technologies has ramped up progress in interpreting germline and somatic mutations and has begun to reshape medicine, especially in oncology. Over the past decade, regulatory agencies realized that they needed to be proactive and not reactive if personalized medicine was to become a reality. The US Food and Drug Administration, in particular, took steps to nurture the field through peer-reviewed publications, co-sponsoring public workshops and issuing guidance for industry. The following two major approaches to personalized medicine were taken: (i) encouragement of de novo co-development of drug-genetic test combinations by industry; and (ii) retrospective assessment of legacy genetic data for the purpose of updating drug labels. The former strategy has been more successful in getting new targeted therapies to the marketplace with successful adoption, while the latter, as evidenced by the low adoption rate of pharmacogenetic testing, has been less successful. This reflection piece makes clear that several important things need to happen to make personalized medicine diffuse in more geographical areas and among more therapeutic specialties. The debate over clinical utility of genetic tests needs to be resolved with consensus on evidentiary standards. Physicians, as gatekeepers of prescription medicines, need to increase their knowledge of genetics and the application of the information to patient care. An infrastructure needs to be developed to make access to genetic tests and decision-support tools available to primary practitioners and specialists outside major medical centres and metropolitan areas.

Identification of Novel and Early Biomarkers for Cisplatin-induced Nephrotoxicity and the Nephroprotective Role of Cimetidine using a Pharmacometabolomic-based Approach Coupled with In Vitro Toxicodynamic Modeling and Simulation.

Cisplatin is widely used for the treatment of various types of cancer. However, cisplatin-induced nephrotoxicity (CIN) is frequently observed in patients receiving cisplatin therapy which poses a challenge in its clinical utility. Currently used clinical biomarkers for CIN are not adequate for early detection of nephrotoxicity, hence there is a need to identify potential early biomarkers in predicting CIN. In the current study, a combination of in vitro toxicodynamic (TD) modeling and untargeted global metabolomics approach was used to identify novel potential metabolite biomarkers for early detection of CIN. In addition, we investigated the protective role of cimetidine (CIM), an inhibitor of the organic cation transporter 2 (OCT2), in suppressing CIN. We first characterized the time-course of nephrotoxic effects of cisplatin (CIS) and the protective effects of CIM in a human pseudo-immortalized renal proximal tubule epithelial cell line (RPTEC), SA7K cell line. Secondly, we used a mathematical cell-level, in vitro TD modeling approach to quantitatively characterize the time-course effects of CIS and CIM as single agents and combination in SA7K cells. Based on the experimental and modeling results, we selected relevant concentrations of CIS and CIM for our metabolomics study. With the help of PCA (Principal Component Analysis) and PLS-DA (Projection to Latent Structure - Discriminate Analysis) analyses, we confirmed global metabolome changes for different groups (CIS, CIM, CIS+CIM vs control) in SA7K cells. Based on the criterion of a p-value ≤ 0.05 and a fold change ≥ 2 or ≤ 0.5, we identified 20 top metabolites that were significantly changed during the early phase i.e. within first 12 h of CIS treatment. Finally, pathway analysis was conducted that revealed the key metabolic pathways that were most impacted in CIN.

Exploring the impact of CYP2D6 and UGT2B7 gene-drug interactions, and CYP-mediated DDI on oxycodone and oxymorphone pharmacokinetics using physiologically-based pharmacokinetic modeling and simulation.

Oxycodone is one of the most commonly used opioids to treat moderate to severe pain. It is metabolized mainly by CYP3A4 and CYP2D6, while only a small fraction of the dose is excreted unchanged into the urine. Oxymorphone, the metabolite primarily formed by CYP2D6, has a 40- to 60-fold higher mu-opioid receptor affinity than the parent compound. While CYP2D6-mediated gene-drug-interactions (GDIs) and drug-drug interactions (DDIs) are well-studied, they only account for a portion of the variability in oxycodone and oxymorphone exposure. The combined impact of CYP2D6-mediated GDIs and DDIs, CYP3A4-mediated DDIs, and UGT2B7 GDIs is not fully understood yet and hard to study in head-to-head clinical trials given the relatively large number of scenarios. Instead, we propose the use of a physiologically-based pharmacokinetic model that integrates available information on oxycodone's metabolism to characterize and predict the impact of DDIs and GDIs on the exposure of oxycodone and its major, pharmacologically-active metabolite oxymorphone. To this end, we first developed and verified a PBPK model for oxycodone and its metabolites using published clinical data. The verified model was then applied to determine the dose-exposure relationship of oxycodone and oxymorphone stratified by CYP2D6 and UGT2B7 phenotypes respectively, and administered perpetrators of CYP-based drug interactions. Our simulations demonstrate that the combination of CYP2D6 UM and a UGT2B7Y (268) mutation may lead to a 2.3-fold increase in oxymorphone exposure compared to individuals who are phenotyped as CYP2D6 NM / UGT2B7 NM. The extent of oxymorphone exposure increases up to 3.2-fold in individuals concurrently taking CYP3A4 inhibitors, such as ketoconazole. Inhibition of the CYP3A4 pathway results in a relative increase in the partial metabolic clearance of oxycodone to oxymorphone. Oxymorphone is impacted to a higher extent by GDIs and DDIs than oxycodone. We predict oxymorphone exposure to be highest in CYP2D6 UMs/UGT2B7 PMs in the presence of ketoconazole (strong CYP3A4 index inhibitor) and lowest in CYP2D6 PMs/UGT2B7 NMs in the presence of rifampicin (strong CYP3A4 index inducer) covering a 55-fold exposure range.

Medical applications of microarray technologies: a regulatory science perspective.

The potential medical applications of microarrays have generated much excitement, and some skepticism, within the biomedical community. Some researchers have suggested that within the decade microarrays will be routinely used in the selection, assessment, and quality control of the best drugs for pharmaceutical development, as well as for disease diagnosis and for monitoring desired and adverse outcomes of therapeutic interventions. Realizing this potential will be a challenge for the whole scientific community, as breakthroughs that show great promise at the bench often fail to meet the requirements of clinicians and regulatory scientists. The development of a cooperative framework among regulators, product sponsors, and technology experts will be essential for realizing the revolutionary promise that microarrays hold for drug development, regulatory science, medical practice and public health.

Utility of a physiologically-based pharmacokinetic (PBPK) modeling approach to quantitatively predict a complex drug-drug-disease interaction scenario for rivaroxaban during the drug review process: implications for clinical practice.

BACKGROUND: Rivaroxaban is an oral Factor Xa inhibitor. The primary objective of this communication was to quantitatively predict changes in rivaroxaban exposure when individuals with varying degrees of renal impairment are co-administered with another drug that is both a P-gp and a moderate CYP3A4 inhibitor. METHODS: A physiologically based pharmacokinetic (PBPK) model was developed to simulate rivaroxaban pharmacokinetics in young (20-45 years) or older (55-65 years) subjects with normal renal function, mild, moderate and severe renal impairment, with or without concomitant use of the combined P-gp and moderate CYP3A4 inhibitor, erythromycin. RESULTS: The simulations indicate that combined factors (i.e., renal impairment and the use of erythromycin) have a greater impact on rivaroxaban exposure than expected when the impact of these factors are considered individually. Compared with normal young subjects taking rivaroxaban, concurrent mild, moderate or severe renal impairment plus erythromycin resulted in 1.9-, 2.4- or 2.6-fold increase in exposure, respectively in young subjects; and 2.5-, 2.9- or 3.0-fold increase in exposure in older subjects. CONCLUSIONS: These simulations suggest that a drug-drug-disease interaction is possible, which may significantly increase rivaroxaban exposure and increase bleeding risk. These simulations render more mechanistic insights as to the possible outcomes and allow one to reach a decision to add cautionary language to the approved product labeling for rivaroxaban.

Pharmacodynamic Modeling to Evaluate the Impact of Cimetidine, an OCT2 Inhibitor, on the Anticancer Effects of Cisplatin.

Despite potent anticancer activity, the clinical utilization of cisplatin is limited due to nephrotoxicity. As Organic Cation Transporter 2 (OCT2) has been shown to be one of the key transporters involved in the uptake of cisplatin into renal proximal tubules, OCT2 inhibitors such as cimetidine have been explored to suppress cisplatin-induced nephrotoxicity. Nonetheless, the impact of OCT2 inhibition or cimetidine on the anti-cancer effects of cisplatin has not been extensively examined. The main objective of the present study was to quantitatively characterize the anticancer effects of cisplatin and cimetidine and determine their nature of interactions in two cancer cell lines, OCT2-negative hepatocellular carcinoma (HCC) cell line, Huh7, and OCT2-positive breast cancer cell line, MDA-MB-468. First, we determined the static concentration-response curves of cisplatin and cimetidine as single agents. Next, with the help of three-dimensional (3D) response surface analyses and a competitive interaction model, we determined their nature of interactions at static concentrations to be modestly synergistic or additive in Huh7 and antagonistic in MDA-MB-468. These results were consistent with the cell-level pharmacodynamic (PD) modeling analysis which leveraged the time-course effects of drugs as single agents and drug combinations. Our developed PD model can be further used to design future preclinical studies to further investigate the cisplatin and cimetidine combinations in different in vitro and in vivo cancer models.

Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports.

Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient-centered approaches for the assessment of drug safety. We anticipate that such advances in real-world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug-induced molecular perturbations and adverse events.

Application of a patient-centered reverse translational systems-based approach to understand mechanisms of an adverse drug reaction of immune checkpoint inhibitors.

Immunotherapy became a key pillar of cancer therapeutics with the approvals of ipilimumab, nivolumab, and pembrolizumab, which inhibit either cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed death-1 (PD-1) that are negative regulators of T-cell activation. However, boosting T-cell activation is often accompanied by autoimmunity, leading to adverse drug reactions (ADRs), including high grade 3-4 colitis and its severe complications whose prevalence may reach 14% for combination checkpoint inhibitors. In this research, we investigated how mechanistic differences between anti-CTLA-4 (ipilimumab) and anti-PD-1 (nivolumab and pembrolizumab) affect colitis, a general class toxicity. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases for hypothesis generation regarding the underlying molecular mechanisms causing colitis. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. We verified that the anti-CTLA-4 drug is associated with an approximately three-fold higher proportional reporting ratio associated with colitis than those of the anti-PD-1 drugs. The signal of the molecular mechanisms, including signaling pathways of inflammatory cytokines, was statistically insignificant to test the hypothesis that the severer rate of colitis associated with ipilimumab would be due to a greater magnitude of T-cell activation as a result of earlier response of the anti-CTLA-4 drug in the immune response. This patient-centered systems-based approach provides an exploratory process to better understand drug pair adverse events at pathway and target levels through reverse translation from postmarket surveillance safety reports.

Brexpiprazole Pharmacokinetics in CYP2D6 Poor Metabolizers: Using Physiologically Based Pharmacokinetic Modeling to Optimize Time to Effective Concentrations.

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia, or as adjunctive treatment for major depressive disorder. As cytochrome P450 (CYP) 2D6 contributes significantly to brexpiprazole metabolism, there is a label-recommended 50% reduction in dose among patients with the CYP2D6 poor metabolizer phenotype. This study uses a whole-body physiologically based pharmacokinetic (PBPK) model to compare the pharmacokinetics of brexpiprazole in patients known to be extensive metabolizers (EMs) and poor metabolizers (PMs). A PBPK model was constructed, verified, and validated against brexpiprazole clinical data, and simulations of 500 subjects were performed to establish the median time to effective concentrations in EMs and PMs. The PBPK simulations captured brexpiprazole PK well and demonstrated significant differences in the time to effective concentrations between EMs and PMs according to the label-recommended titration. Additionally, these simulations suggest that CYP2D6 PMs consistently achieve lower minimum concentrations during the dosing interval than CYP2D6 EMs. Simulations using an alternative dosing strategy of twice-daily dosing (as opposed to once daily) in PMs during the first week of brexpiprazole dosing yielded more consistent plasma concentrations between EMs and PMs, without exceeding the area under the plasma concentration-time curve observed in the EMs. Taken together, the results of these PBPK simulations suggest that product labeling for brexpiprazole titration in CYP2D6 PMs likely overcompensates for the decreased clearance seen in this population. We propose an alternative dosing strategy that decreases the time to effective concentrations and recommend a reevaluation of steady-state PK in this population to potentially allow for higher daily doses in CYP2D6 PMs.

A case study of a patient-centered reverse translational systems-based approach to understand adverse event profiles in drug development.

Adverse drug reactions (ADRs) of targeted therapy drugs (TTDs) are frequently unexpected and long-term toxicities detract from exceptional efficacy of new TTDs. In this proof-of-concept study, we explored how molecular causation involved in trastuzumab-induced cardiotoxicity changes when trastuzumab was given in combination with doxorubicin, tamoxifen, paroxetine, or lapatinib. The data analytical platform Molecular Health Effect was utilized to map population ADR data from the US Food and Drug Administration (FDA) Adverse Event Reporting System to chemical and biological databases (such as UniProt and Reactome), for hypothesis generation regarding the underlying molecular mechanisms causing cardiotoxicity. Disproportionality analysis was used to assess the statistical relevance between adverse events of interest and molecular causation. Literature search was performed to compare the established hypotheses to published experimental findings. We found that the combination therapy of trastuzumab and doxorubicin may affect mitochondrial dysfunction in cardiomyocytes through different molecular pathways such as BCL-X and PGC-1α proteins, leading to a synergistic effect of cardiotoxicity. We found, on the other hand, that trastuzumab-induced cardiotoxicity would be diminished by concomitant use of tamoxifen, paroxetine, and/or lapatinib. Tamoxifen and paroxetine may cause less cardiotoxicity through an increase in antioxidant activities, such as glutathione conjugation. Lapatinib may decrease the apoptotic effects in cardiomyocytes by altering the effects of trastuzumab on BCL-X proteins. This patient-centered systems-based approach provides, based on the trastuzumab-induced ADR cardiotoxicity, an example of how to apply reverse translation to investigate ADRs at the molecular pathway and target level to understand the causality and prevalence during drug development of novel therapeutics.

Dosing Regimen Prediction and Confirmation With Rivaroxaban for Thromboprophylaxis in Children After the Fontan Procedure: Insights From the Phase III UNIVERSE Study.

Thrombosis remains an important complication for children with single-ventricle physiology following the Fontan procedure, and effective thromboprophylaxis is an important unmet medical need. To obviate conventional dose-finding studies and expedite clinical development, a rivaroxaban dose regimen for this indication was determined using a model-informed drug development approach. A physiologically based pharmacokinetic rivaroxaban model was used to predict a pediatric dosing regimen that would produce drug exposures similar to that of 10 mg once daily in adults. This regimen was used in an open-label, multicenter phase III study, which investigated the use of rivaroxaban for thromboprophylaxis in post-Fontan patients 2 to 8 years of age. The pharmacokinetics (PK) of rivaroxaban was assessed in part A (n = 12) and in part B (n = 64) of the UNIVERSE study. The safety and efficacy in the rivaroxaban group were compared to those in the acetylsalicylic acid group for 12 months. Pharmacodynamic end points were assessed in both parts of the study. Rivaroxaban exposures achieved in parts A and B were similar to the adult reference exposures. Prothrombin time also showed similarity to the adult reference. Exposure-response analysis did not identify a quantitative relationship between rivaroxaban exposures and efficacy/safety outcomes within the observed exposure ranges. A body weight-based dose regimen selected by physiologically based pharmacokinetic modeling was shown in the UNIVERSE study to be appropriate for thromboprophylaxis in the post-Fontan pediatric population. Model-based dose selection can support pediatric drug development and bridge adult dose data to pediatrics, thereby obviating the need for dose-finding studies in pediatric programs.

Perspective on model-informed drug development.

Model-informed drug development (MIDD) is a process intended to expedite drug development, enhance regulatory science, and produce benefits for patients. Quantitative modeling and simulation-principally by population pharmacokinetics (PK), exposure-response, and physiologically based pharmacokinetic (PBPK) analysis-is the technology that provides the capability to deploy MIDD across a range of applications. MIDD was codified in the 2017 Prescription Drug User Fee Act Reauthorization 1 (PDUFA VI, 2018-2022) and a performance goal was a MIDD pilot program to hold 2 to 4 industry-U.S. Food and Drug Administration (FDA) paired meetings quarterly through 2022.

Physiologically-based pharmacokinetics modeling to investigate formulation factors influencing the generic substitution of dabigatran etexilate.

The exposure-response relationship of direct acting oral anti-coagulants (DOACs) for bleeding risk is steep relative to ischemic stroke reduction. As a result, small changes in exposure may lead to bleeding events. The overall goal of this project was to determine the effect of critical formulation parameters on the pharmacokinetics (PKs) and thus safety and efficacy of generic DOACs. In this first installment of our overall finding, we developed and verified a physiologically-based PK (PBPK) model for dabigatran etexilate (DABE) and its metabolites. The model was developed following a middle out approach leveraging available in vitro and in vivo data. External validity of the model was confirmed by overlapping predicted and observed PK profiles for DABE as well as free and total dabigatran for a dataset not used during model development. The verified model was applied to interrogate the impact of modulating the microenvironment pH on DABE systemic exposure. The PBPK exploratory analyses highlighted the high sensitivity of DABE exposure to supersaturation ratio and precipitation kinetics.

Quantitative Systems Pharmacology Model-Based Predictions of Clinical Endpoints to Optimize Warfarin and Rivaroxaban Anti-Thrombosis Therapy.

BACKGROUND: Tight monitoring of efficacy and safety of anticoagulants such as warfarin is imperative to optimize the benefit-risk ratio of anticoagulants in patients. The standard tests used are measurements of prothrombin time (PT), usually expressed as international normalized ratio (INR), and activated partial thromboplastin time (aPTT). OBJECTIVE: To leverage a previously developed quantitative systems pharmacology (QSP) model of the human coagulation network to predict INR and aPTT for warfarin and rivaroxaban, respectively. METHODS: A modeling and simulation approach was used to predict INR and aPTT measurements of patients receiving steady-state anticoagulation therapy. A previously developed QSP model was leveraged for the present analysis. The effect of genetic polymorphisms known to influence dose response of warfarin (CYP2C9, VKORC1) were implemented into the model by modifying warfarin clearance (CYP2C9 *1: 0.2 L/h; *2: 0.14 L/h, *3: 0.04 L/h) and the concentration of available vitamin K (VKORC1 GA: -22% from normal vitamin K concentration; AA: -44% from normal vitamin K concentration). Virtual patient populations were used to assess the ability of the model to accurately predict routine INR and aPTT measurements from patients under long-term anticoagulant therapy. RESULTS: The introduced model accurately described the observed INR of patients receiving long-term warfarin treatment. The model was able to demonstrate the influence of genetic polymorphisms of CYP2C9 and VKORC1 on the INR measurements. Additionally, the model was successfully used to predict aPTT measurements for patients receiving long-term rivaroxaban therapy. CONCLUSION: The QSP model accurately predicted INR and aPTT measurements observed during routine therapeutic drug monitoring. This is an exemplar of how a QSP model can be adapted and used as a model-based precision dosing tool during clinical practice and drug development to predict efficacy and safety of anticoagulants to ultimately help optimize anti-thrombotic therapy.

Semi-mechanistic modelling platform to assess cardiac contractility and haemodynamics in preclinical cardiovascular safety profiling of new molecular entities.

BACKGROUND AND PURPOSE: Cardiovascular safety is one of the most frequent causes of safety-related attrition both preclinically and clinically. Preclinical cardiovascular safety is routinely assessed using dog telemetry monitoring key cardiovascular functions. The present research was to develop a semi-mechanistic modelling platform to simultaneously assess changes in contractility (dPdtmax ), heart rate (HR) and mean arterial pressure (MAP) in preclinical studies. EXPERIMENTAL APPROACH: Data from dPdtmax , HR, preload (left ventricular end-diastolic pressure [LVEDP]) and MAP were available from dog telemetry studies after dosing with atenolol (n = 27), salbutamol (n = 5), L-NG -nitroarginine methyl ester (L-NAME; n = 4), milrinone (n = 4), verapamil (n = 12), dofetilide (n = 8), flecainide (n = 4) and AZ001 (n = 14). Literature model for rat CV function was used for the structural population pharmacodynamic model development. LVEDP was evaluated as covariate to account for the effect of preload on dPdtmax . KEY RESULTS: The model was able to describe drug-induced changes in dPdtmax , HR and MAP for all drugs included in the developed framework adequately, by incorporating appropriate drug effects on dPdtmax , HR and/or total peripheral resistance. Consistent with the Starling's law, incorporation of LVEDP as a covariate on dPdtmax to correct for the preload effect was found to be statistically significant. CONCLUSIONS AND IMPLICATIONS: The contractility and haemodynamics semi-mechanistic modelling platform accounts for diurnal variation, drug-induced changes and inter-animal variation. It can be used to hypothesize and evaluate pharmacological effects and provide a holistic cardiovascular safety profile for new drugs.