Using virtual populations to solve drug development problems. Iain Gardner Head of Translational DMPK Science SIMCYP
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1 Using virtual populations to solve drug development problems Iain Gardner Head of Translational DMPK Science SIMCYP
2 Prediction of human PK (PD) in virtual individuals In vitro system In vitro CLu int Scaling Factor (MPGGL, HPGL) CLu int per g Liver Liver weight CLu int per Liver Simcyp approach Combine in vitro-in vivo extrapolation (IVIVE) and PBPK approaches in virtual individuals to predict drug concentration and effect Identifying relevant DISTRIBUTION of values for demographical, biological, physiological and genetic parameters in target population & the COVARIATIONS between the parameters in target POPULATION Copyright 2015 Certara, L.P. All rights reserved. 2
3 Separating systems & drug information (Systems Pharmacology) Systems Data Age Weight Tissue Volumes Tissue Composition Cardiac Output Tissue Blood Flows [Plasma Protein] Drug Data MW LogP pka Protein binding BP ratio In vitro Metabolism Permeability Solubility Trial Design Dose Administration route Frequency Co-administered drugs Populations Mechanistic IVIVE linked PBPK models Prediction of ADME in population of interest Jamei et al., DMPK, 2009, Rostami-Hodjegan, CPT, 2012 Copyright 2015 Certara, L.P. All rights reserved.
4 Why ADME properties are important for drugs? ADME properties determine the drug concentration in the blood and the concentration that can reach the bacteria in the lung granuloma Absorption Distribution Metabolism Excretion CYP UGT NAT PgP CYP3A Metabolises ~50% of marketed drugs Subject to drug interactions (DDI) - Can be inhibited drug concentrations - Can be induced drug concentrations Copyright 2015 Certara, L.P. All rights reserved. 4
5 Prospective DDI prediction Induction (Wagner, Clin PK, 54, 117, 2015; Clin Pk, 2015) Copyright 2015 Certara, L.P. All rights reserved. 5
6 PBPK Submissions to FDA (Courtesy of Dr Joe Grillo; AAPS Nov 2014) Modified from Huang SM, et al. J Pharm Sci, Updated October 22, 2014 (136 submissions) Copyright 2015 Certara, L.P. All rights reserved. 6
7 Growing impact of PBPK on drug labels Olysio (Simeprevir) Xarelto (Rivaroxaban) Edurant (Rilpivirine) Imbruvia (Ibrutinib) Opsumit (Macitentan) Hepatitis C Thrombosis & Embolism HIV infection Lymphoma and Leukemia Pulmonary Hypertension Zykadia (Ceritinbi) Odozmzo (Sonidegib) Farydak (Panobinostat) Revatio (Sildenafil) Bosulif (Bosutinib) Lung Cancer Basal Cell Carcinoma Multiple myeloma Pulmonary Hypertension Myelogenous Leukemia Lynparza (Olaparib) Movantik (Naloxegol) Tagrisso (Osimertinib) Iclusig (Ponatinib) Cerdelga (Eliglustat) Advanced Ovarian Cancer Opioid Induced Constipation Metastatic NSCLC Chronic Myeloid Leukemia Gaucher Disease Jevtana (Cabazitaxel) Cotellic (Cobimetinib) Lenvima (Lenvatinib) Aristada (Aripiprazole) Prostate Cancer Metastatic Melanoma Thyroid cancer Schizophrenia Copyright 2015 Certara, L.P. All rights reserved. 7
8 Ibrutinib Key information: CYP3A substrate CL/F = 1000 L/h Susceptibility to DDI is high Copyright 2015 Certara, L.P. All rights reserved. 8
9 Ibrutinib Wording in the Label DRUG INTERACTIONS Coadministration of ibrutinib with CYP3A4 inhibitors Simulations using physiologically based pharmacokinetic (PBPK) models suggested that a moderate CYP3A inhibitor (diltiazem and erythromycin) may increase the AUC of ibrutinib 6 to 9-fold. Coadministration of ibrutinib with CYP3A4 inducers Simulations using PBPK suggested that a moderate CYP3A inducer (efavrirenz) may decrease the AUC of ibrutinib by up to 3-fold. Copyright 2015 Certara, L.P. All rights reserved. 9
10 Dose (mg) Can PBPK Provide Dose Optimisation Strategy? CYP3A Inhibitors Recommended dose is 560 mg (QD) Highest dose evaluated ( mg) safety issues Reduce IMBRUVICA dose to 140 mg if a moderate CYP3A inhibitor must be used (e.g., fluconazole, darunavir, erythromycin, diltiazem, atazanavir, aprepitant, amprenavir, fosamprevir, crizotinib, imatinib, verapamil, grapefruit products and ciprofloxacin) 10-fold Predicted DDI was 5 to 8-fold with moderate CYP3A4 inhibitors Copyright 2015 Certara, L.P. All rights reserved.
11 Physiologically-based pharmacokinetic model Components: Lung model (with granulomas) Virtual South African population Compound files Pyrazinamide, rifampicin, ethambutol, isoniazid, bedaquiline, moxifloxacin, delaminid, pretomanid (PA-824) Application: Prediction of results first-in-human trials Optimization of dose selection Quantification of drug-drug interaction potential 11
12 Physiologically-based pharmacokinetic models: Lung model component that incorporates pathophysiological changes related to TB infection 12
13 Venous Blood Arterial Blood High-level PBPK representation of a human Lung Generic Adipose Bone Brain Heart Kidney Muscle Skin Liver Spleen Portal Vein Pancreas IV EHC Gut PO 13
14 Zoom into a PBPK description of the lungs Airways Alveoli Epithelial-lining Fluid Tissue mass Capilaries Gaohua L, Wedagedera J, Small BG, Almond L, Romero K, Hermann D, Hanna D, Jamei M, Gardner I. Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs. CPT Pharmacometrics Syst Pharmacol. 14
15 Zoom into a PBPK description of pulmonary granulomas Granuloma Air Air Rim- Mass Fluid Fluid Caseum2 Caseum1 Rim-ISF Mass Mass Rim- Blood Blood Blood PBR (D artois; Nature Rev. Microbiol., 12, 159, 2014) 15
16 Physiologically-based pharmacokinetic models: Virtual South African population component that also accounts for TB-related changes 16
17 Height (cm) Virtual South African Population Age-Distribution Age-Height 200 Height-Weight Demographics Body size Age (y) Observed CYP3A5 NAT-2 Body Surface Area Nwoye model Observed PM EM Genetics SLOW Albumin INTERMEDIATE FAST Haematocrit Male Observed
18 Physiology changes in TB BMI Weight AAG Lung ph Albumin Haematocrit Healthy TB-infected Male
19 Compound files 19
20 Simulated/predicted results for different doses of moxifloxacin Gaohua L, Wedagedera J, Small BG, Almond L, Romero K, Hermann D, Hanna D, Jamei M, Gardner I. Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs. CPT Pharmacometrics Syst Pharmacol. 20
21 The model accounts for relevant aspects of drug distribution 400mg PO lung tissue : plasma ratio Epithelial lining fluid : plasma ratio Observed Predicted Moxifloxacin Gaohua L, Wedagedera J, Small BG, Almond L, Romero K, Hermann D, Hanna D, Jamei M, Gardner I. Development of a Multicompartment Permeability-Limited Lung PBPK Model and Its Application in Predicting Pulmonary Pharmacokinetics of Antituberculosis Drugs. CPT Pharmacometrics Syst Pharmacol. 21
22 Scaling P-gp effect for moxifloxacin In vitro permeability data was extracted and analysed to obtain transporter intrinsic clearance (36.1 ml/min) Scaled to the whole lung accounting for differences in surface area between in vitro and in vivo situation Clinical Simulations - P-gp + P-gp + P-gp (x2) Copyright 2015 Certara, L.P. All rights reserved. 22
23 How can PBPK simulations help? Granuloma Effect What will happen if co-medications cause DDI and alter the concentration of my drug? What effect will food have on plasma levels and effect? What will happen to plasma concentrations in subjects with different ethnicity, disease states, ages? Copyright 2015 Certara, L.P. All rights reserved.
24 Conclusions PBPK modelling used routinely to predict DDI in clinic Submissions to regulatory agency Used in lieu of clinical studies in Drug labels Tools developed to allow a similar approach to be used in predicting PK, lung disposition and DDI of TB drugs Need to develop case studies to demonstrate clinical utility Show applicability of these M+S approaches Training workshop in use of available PBPK tools August 2016, Princeton Copyright 2015 Certara, L.P. All rights reserved. 24
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