Leveraging model-informed approaches for drug discovery and development in the cardiovascular space

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

[1]  Paramjeet Kaur,et al.  Applications of In Vitro–In Vivo Correlations in Generic Drug Development: Case Studies , 2015, The AAPS Journal.

[2]  S. Zhou,et al.  Slow drug delivery decreased total body clearance and altered bioavailability of immediate‐ and controlled‐release oxycodone formulations , 2016, Pharmacology research & perspectives.

[3]  S. Visser,et al.  Implementation of Quantitative and Systems Pharmacology in Large Pharma , 2014, CPT: pharmacometrics & systems pharmacology.

[4]  S. Visser,et al.  Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery. , 2015, Future medicinal chemistry.

[5]  Y. Gebremichael,et al.  A Quantitative Systems Physiology Model of Renal Function and Blood Pressure Regulation: Application in Salt‐Sensitive Hypertension , 2017, CPT: pharmacometrics & systems pharmacology.

[6]  I. Rajman PK/PD modelling and simulations: utility in drug development. , 2008, Drug discovery today.

[7]  C Garnett,et al.  Results From the IQ‐CSRC Prospective Study Support Replacement of the Thorough QT Study by QT Assessment in the Early Clinical Phase , 2015, Clinical pharmacology and therapeutics.

[8]  Ramesh Sarangapani,et al.  A model-based approach to investigating the pathophysiological mechanisms of hypertension and response to antihypertensive therapies: extending the Guyton model. , 2014, American journal of physiology. Regulatory, integrative and comparative physiology.

[9]  Gary Gintant,et al.  Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. , 2014, American heart journal.

[10]  F. Rosendaal,et al.  Decreased mortality of ischaemic heart disease among carriers of haemophilia , 2003, The Lancet.

[11]  D. Atar,et al.  Apixaban versus warfarin in patients with atrial fibrillation. , 2011, The New England journal of medicine.

[12]  Model-Based Decision Making in Early Clinical Development: Minimizing the Impact of a Blood Pressure Adverse Event , 2009, The AAPS Journal.

[13]  S. Darby,et al.  Mortality rates, life expectancy, and causes of death in people with hemophilia A or B in the United Kingdom who were not infected with HIV. , 2007, Blood.

[14]  Manash S. Chatterjee,et al.  Preclinical and translational evaluation of coagulation factor IXa as a novel therapeutic target , 2016, Pharmacology research & perspectives.

[15]  D. Mehrotra,et al.  Enabling robust assessment of QTc prolongation in early phase clinical trials , 2017, Pharmaceutical statistics.

[16]  M. Fornage,et al.  Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association , 2017, Circulation.

[17]  D. Tatosian,et al.  Pharmacokinetics and Pharmacodynamics of Omarigliptin, a Once‐Weekly Dipeptidyl Peptidase‐4 (DPP‐4) Inhibitor, After Single and Multiple Doses in Healthy Subjects , 2016, Journal of clinical pharmacology.

[18]  R. Troughton,et al.  Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. , 2011, The New England journal of medicine.

[19]  D. Bloomfield Incorporating exposure‐response modeling into the assessment of QTc interval: A potential alternative to the thorough QT study , 2015, Clinical pharmacology and therapeutics.

[20]  Filippos Kesisoglou,et al.  Development of in vitro-in vivo correlation for extended-release niacin after administration of hypromellose-based matrix formulations to healthy volunteers. , 2014, Journal of pharmaceutical sciences.

[21]  A. Adewale,et al.  Prediction of Clinical Irrelevance of PK Differences in Atorvastatin Using PK/PD Models Derived From Literature-Based Meta-Analyses , 2014, Clinical pharmacology and therapeutics.

[22]  A. Bergman,et al.  Model-Based Development of Anacetrapib, a Novel Cholesteryl Ester Transfer Protein Inhibitor , 2011, The AAPS Journal.

[23]  M. Landray,et al.  Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease , 2017, The New England journal of medicine.

[24]  Saroja Ramanujan,et al.  Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach[S] , 2016, Journal of Lipid Research.

[25]  S. Yusuf,et al.  Dabigatran versus warfarin in patients with atrial fibrillation. , 2009, The New England journal of medicine.

[26]  CJ Musante,et al.  Quantitative Systems Pharmacology: A Case for Disease Models , 2016, Clinical pharmacology and therapeutics.

[27]  M. Gibbs,et al.  Model‐Based Meta‐Analysis for Comparative Efficacy and Safety: Application in Drug Development and Beyond , 2011, Clinical pharmacology and therapeutics.

[28]  D. Tatosian,et al.  A Thorough QTc Study Confirms Early Pharmacokinetics/QTc Modeling: A Supratherapeutic Dose of Omarigliptin, a Once‐Weekly DPP‐4 Inhibitor, Does Not Prolong the QTc Interval , 2016, Clinical pharmacology in drug development.

[29]  James Lu,et al.  An In-Silico Model of Lipoprotein Metabolism and Kinetics for the Evaluation of Targets and Biomarkers in the Reverse Cholesterol Transport Pathway , 2014, PLoS Comput. Biol..