Emerging Role of Organ‐on‐a‐Chip Technologies in Quantitative Clinical Pharmacology Evaluation

The recently enacted Prescription Drug User Fee Act (PDUFA) VI includes in its performance goals “enhancing regulatory science and expediting drug development.” The key elements in “enhancing regulatory decision tools to support drug development and review” include “advancing model‐informed drug development (MIDD).” This paper describes (i) the US Food and Drug Administration (FDA) Office of Clinical Pharmacology's continuing efforts in developing quantitative clinical pharmacology models (disease, drug, and clinical trial models) to advance MIDD, (ii) how emerging novel tools, such as organ‐on‐a‐chip technologies or microphysiological systems, can provide new insights into physiology and disease mechanisms, biomarker identification and evaluation, and elucidation of mechanisms of adverse drug reactions, and (iii) how the single organ or linked organ microphysiological systems can provide critical system parameters for improved physiologically‐based pharmacokinetic and pharmacodynamic evaluations. Continuous public‐private partnerships are critical to advance this field and in the application of these new technologies in drug development and regulatory review.

[1]  Yaning Wang,et al.  Commentary on Fit-For-Purpose Models for Regulatory Applications. , 2019, Journal of pharmaceutical sciences.

[2]  Orlando S. Hoilett,et al.  Metabolic consequences of inflammatory disruption of the blood-brain barrier in an organ-on-chip model of the human neurovascular unit , 2016, Journal of Neuroinflammation.

[3]  Patrick Poulin,et al.  Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods. , 2009, Journal of pharmaceutical sciences.

[4]  Gang Wang,et al.  Modeling the mitochondrial cardiomyopathy of Barth syndrome with iPSC and heart-on-chip technologies , 2014 .

[5]  Jong Hwan Sung,et al.  Using physiologically-based pharmacokinetic-guided “body-on-a-chip” systems to predict mammalian response to drug and chemical exposure , 2014, Experimental biology and medicine.

[6]  Albert P. Li,et al.  Human Enterocytes as an In Vitro Model for the Evaluation of Intestinal Drug Metabolism: Characterization of Drug-Metabolizing Enzyme Activities of Cryopreserved Human Enterocytes from Twenty-Four Donors , 2017, Drug Metabolism and Disposition.

[7]  Shiew-Mei Huang,et al.  Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration's Office of Clinical Pharmacology. , 2019, Journal of pharmaceutical sciences.

[8]  Shiew-Mei Huang,et al.  In Vitro-In Vivo Extrapolation of Metabolism- and Transporter-Mediated Drug-Drug Interactions-Overview of Basic Prediction Methods. , 2017, Journal of pharmaceutical sciences.

[9]  Murat Cirit,et al.  Maximizing the impact of microphysiological systems with in vitro-in vivo translation. , 2018, Lab on a chip.

[10]  Ohidul Siddiqui,et al.  Endpoints and Analyses to Discern Disease-Modifying Drug Effects in Early Parkinson’s Disease , 2009, The AAPS Journal.

[11]  Roger D Kamm,et al.  Screening therapeutic EMT blocking agents in a three-dimensional microenvironment. , 2013, Integrative biology : quantitative biosciences from nano to macro.

[12]  Mandy B. Esch,et al.  Characterization of a gastrointestinal tract microscale cell culture analog used to predict drug toxicity , 2009, Biotechnology and bioengineering.

[13]  Giorgia Del Favero,et al.  Integrating Biophysics in Toxicology , 2020, Cells.

[14]  Richard Novak,et al.  Matched-Comparative Modeling of Normal and Diseased Human Airway Responses Using a Microengineered Breathing Lung Chip. , 2016, Cell systems.

[15]  D. Ingber,et al.  Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. , 2012, Lab on a chip.

[16]  I. Zineh,et al.  Clinical Drug-Drug Interaction Evaluations to Inform Drug Use and Enable Drug Access. , 2017, Journal of pharmaceutical sciences.

[17]  Aleksandra Galetin,et al.  Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance , 2016, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[18]  B P Booth,et al.  Elucidation of Relationship Between Tumor Size and Survival in Non‐Small‐Cell Lung Cancer Patients Can Aid Early Decision Making in Clinical Drug Development , 2009, Clinical pharmacology and therapeutics.

[19]  Martin L Yarmush,et al.  Building and manipulating neural pathways with microfluidics. , 2010, Lab on a chip.

[20]  Luke P. Lee,et al.  Human iPSC-based Cardiac Microphysiological System For Drug Screening Applications , 2015, Scientific Reports.

[21]  N. Uemura.  From Molecule to Patient: Building Bridges Not Walls with Clinical Pharmacology and Translational Medicine , 2019, Clinical and translational science.

[22]  I. Zineh,et al.  Considerations for Developing Targeted Therapies in Low‐Frequency Molecular Subsets of a Disease , 2018, Clinical pharmacology and therapeutics.

[23]  Danny D Shen,et al.  Development of a microphysiological model of human kidney proximal tubule function. , 2016, Kidney international.

[24]  Albert Gough,et al.  A human liver microphysiology platform for investigating physiology, drug safety, and disease models , 2016, Experimental biology and medicine.

[25]  Jong Hwan Sung,et al.  Using PBPK guided “ Body-ona-Chip ” Systems to Predict Mammalian Response to Drug and Chemical Exposure , 2014 .

[26]  Yaning Wang,et al.  Quantification of disease progression and dropout for Alzheimer's disease. , 2013, International journal of clinical pharmacology and therapeutics.

[27]  Kyall J. Pocock,et al.  Intestine-on-a-Chip Microfluidic Model for Efficient in Vitro Screening of Oral Chemotherapeutic Uptake. , 2017, ACS biomaterials science & engineering.

[28]  G. Hamilton,et al.  Organs‐on‐Chips in Clinical Pharmacology: Putting the Patient Into the Center of Treatment Selection and Drug Development , 2019, Clinical pharmacology and therapeutics.

[29]  Jin-Ming Lin,et al.  Characterization of drug permeability in Caco-2 monolayers by mass spectrometry on a membrane-based microfluidic device. , 2013, Lab on a chip.

[30]  Lawrence J Lesko,et al.  Quantitative disease, drug, and trial models. , 2009, Annual review of pharmacology and toxicology.

[31]  Murat Cirit,et al.  Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies , 2018, Scientific Reports.

[32]  Priyank V. Kumar,et al.  Organ-on-a-Chip: Opportunities for Assessing the Toxicity of Particulate Matter , 2020, Frontiers in Bioengineering and Biotechnology.

[33]  Kristin M. Fabre,et al.  Organs-on-chips (microphysiological systems): tools to expedite efficacy and toxicity testing in human tissue , 2014, Experimental biology and medicine.

[34]  Ying Zheng,et al.  Innovations in preclinical biology: ex vivo engineering of a human kidney tissue microperfusion system , 2013, Stem Cell Research & Therapy.

[35]  O. B. Usta,et al.  Metabolic Patterning on a Chip: Towards in vitro Liver Zonation of Primary Rat and Human Hepatocytes , 2018, Scientific Reports.

[36]  S. J. Schrieber,et al.  Role of Modeling and Simulation in the Development of Novel and Biosimilar Therapeutic Proteins. , 2019, Journal of pharmaceutical sciences.

[37]  Nina Isoherranen,et al.  Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance , 2018, CPT: pharmacometrics & systems pharmacology.

[38]  Neil Benson,et al.  Systems Pharmacology: Bridging Systems Biology and Pharmacokinetics-Pharmacodynamics (PKPD) in Drug Discovery and Development , 2011, Pharmaceutical Research.

[39]  James R. Broughman,et al.  Gastrointestinal microphysiological systems , 2017, Experimental biology and medicine.

[40]  K. Giacomini,et al.  The International Transporter Consortium: Summarizing Advances in the Role of Transporters in Drug Development , 2018, Clinical pharmacology and therapeutics.

[41]  Jingjing Yu,et al.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification , 2015, Drug Metabolism and Disposition.

[42]  Raffaella Corvi,et al.  3S – Systematic, Systemic, and Systems Biology and Toxicology , 2019, ALTEX.

[43]  L. Griffith,et al.  Quantitative Assessment of Population Variability in Hepatic Drug Metabolism Using a Perfused Three-Dimensional Human Liver Microphysiological System , 2017, The Journal of Pharmacology and Experimental Therapeutics.

[44]  Ravi Iyengar,et al.  Quantitative and Systems Pharmacology in the Post-genomic Era : New Approaches to Discovering Drugs and Understanding Therapeutic , 2011 .

[45]  Yuichi Sugiyama,et al.  Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro, Imaging and Physiologically Based Pharmacokinetic Modeling Approaches , 2018, Clinical pharmacology and therapeutics.

[46]  M. Gastonguay,et al.  Modeling and simulation of the exposure–response and dropout pattern of guanfacine extended-release in pediatric patients with ADHD , 2015, Journal of Pharmacokinetics and Pharmacodynamics.

[47]  Development of a placebo effect model combined with a dropout model for bipolar disorder , 2013, Journal of Pharmacokinetics and Pharmacodynamics.

[48]  Yaning Wang,et al.  Leveraging Prior Quantitative Knowledge to Guide Drug Development Decisions and Regulatory Science Recommendations: Impact of FDA Pharmacometrics During 2004–2006 , 2008, Journal of clinical pharmacology.

[49]  Cécile Legallais,et al.  Metabolomics-on-a-chip of hepatotoxicity induced by anticancer drug flutamide and Its active metabolite hydroxyflutamide using HepG2/C3a microfluidic biochips. , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[50]  K. Suh,et al.  A multi-layer microfluidic device for efficient culture and analysis of renal tubular cells. , 2010, Lab on a chip.

[51]  L. Smirnova,et al.  Food for Thought . . . 3 S – Systematic , Systemic , and Systems Biology and Toxicology , 2018 .

[52]  P. Sager,et al.  Workshop Report: FDA Workshop on Improving Cardiotoxicity Assessment With Human-Relevant Platforms. , 2019, Circulation research.

[53]  M. Rowland,et al.  Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. , 2006, Journal of pharmaceutical sciences.

[54]  Jeonghwan Lee,et al.  Kidney-on-a-Chip: A New Technology for Predicting Drug Efficacy, Interactions, and Drug-induced Nephrotoxicity. , 2018, Current drug metabolism.

[55]  Lei Zhang,et al.  Transporters in Drug Development: Scientific and Regulatory Considerations , 2018, Clinical pharmacology and therapeutics.

[56]  T. Neumann,et al.  Human liver-kidney model elucidates the mechanisms of aristolochic acid nephrotoxicity. , 2017, JCI insight.

[57]  E. Leclerc,et al.  Investigation of ifosfamide nephrotoxicity induced in a liver–kidney co‐culture biochip , 2013, Biotechnology and bioengineering.

[58]  Albert Gough,et al.  Functional Coupling of Human Microphysiology Systems: Intestine, Liver, Kidney Proximal Tubule, Blood-Brain Barrier and Skeletal Muscle , 2017, Scientific Reports.

[59]  M. Yarmush,et al.  A microfluidic hepatic coculture platform for cell-based drug metabolism studies. , 2010, Biochemical pharmacology.

[60]  D. Ingber,et al.  A human breathing lung‐on‐a‐chip , 2010, Annals of the American Thoracic Society.

[61]  Shiew-Mei Huang,et al.  The utility of modeling and simulation in drug development and regulatory review. , 2013, Journal of pharmaceutical sciences.