Building innovative drug discovery alliances Hepatic uptake and drug disposition o in vitro and in silico approaches Dr Beth Williamson Evotec AG, 2017
Outline Importance of predicting clearance In vitro in vivo extrapolation (IVIVE) Reasons for the lack of IVIVE Tools for predicting transporter interaction Transporters in drug discovery Uptake/media loss model In silico predictions PAGE 1
Clearance Importance Within DMPK, predicting in vivo clearance from in vitro data is of paramount importance Accurately predicting human in vivo pharmacokinetics (PK) and dose is one of the most challenging aspects of DMPK However, in vitro in vivo extrapolations (IVIVE) show that the industry-standard hepatocyte stability assay can under-predict in vivo clearance 5 4 3 2 One of the primary causes of this under-prediction is drug 1 transporter interactions which can lead to cellular accumulation these interactions are not accounted for in 0 conventional hepatocyte stability assays 0 1 2 3 4 5 Log in vitro CL int,u (ml/min/kg) IVIVE gives confidence in understanding the clearance mechanisms and the projection of human PK & PK/PD PAGE 2 Di et al., 2013 Riley et al., 2005 oars et al., 2007
Factors to consider when no IVIVE exists 4 Under prediction In vitro conditions? Biliary or renal clearance? Transporter- mediated hepatic uptake e.g. OATPs? Extra hepatic metabolism Log (Observ ved CL int, u) 3 2 1 0-1 -2-2 -1 0 1 2 3 4 Over prediction Enterohepatic recirculation? Analytical considerations Log (Predicted CL int,u ) PAGE 3
Factors to consider when no IVIVE exists Under prediction Identified following rodent PK Little investigation of causes Compound deprioritised Log (Observ ved CL int, u) 4 3 2 1 0-1 -2-2 -1 0 1 2 3 4 Log (Predicted CL int,u ) PAGE 4
Biopharmaceutics Drug Disposition Classification ystem (BDDC) A tool to forecast transporter involvement BDDC separates compounds based on their solubility and permeability Insightful in predicting: Drug-transporter interaction The route of elimination Central nervous system (CN) exposure Drug-drug interactions (DDIs) With an increased understanding of CYP liabilities and the advent of new drug targets, a shift in medicinal chemistry has increased the likelihood Perm eability/ /metabo olism Minimal transporter effects Uptake transporters Efflux transporters (gut) and uptake in liver Uptake and efflux transporters of transporter interactions PAGE 5 Benet 2013
Biopharmaceutics Drug Disposition Classification ystem (BDDC) Marketed and pipeline compounds >90% of the administered dose is absorbed Perm meability y/metab bolism Highest dose soluble in 250ml aq (ph1-7.5) PAGE 6 Benet et al., 2011 Ranard et al., 2013 Benet 2013 Trend of new chemistry moving into transporter space
ADMET Efficiency Index (AEI) Can we propose an indices that quantifies the risk of this PA/LogP Analysis of multiple data sets led the proposal AEI index Modified LLE iscuity an Prom Medi AEI = (pactivity - LogP ) PA x 100 Normalised for PA Algorithm benefits from LLE which has been demonstrated to be effective in reducing attrition through a secondary pharmacology argument (non-specific in nature) PA term is used to score the data based on transporter interactions (BDDC Class III, moderate lipophilicity and propensity for H-bond interaction) PAGE 7 Barton and Riley 2016
ADMET Efficiency Index (AEI) Decision making with AEI The aim of the AEI index is not to restrict the AEI cale properties of new compounds to regions considered as marketed drug space <4 = Likely transporter interaction 4-7 = Possible transporter interaction AEI act as an indicator to highlight the potential deficiencies or challenges in a compound series >7 = No transporter flag AEI serves to highlight concerns that small molecule drug-discovery needs to address in the early stages of the project rather than leaving late stage attrition to continue to take its toll on the industry Examples Atorvastatin AEI = 2.3 aquinavir AEI = 3.8 Ambrisentan AEI = 5.9 PAGE 8 Barton and Riley 2016
Drug transporter studies Why? The potential for transporter-mediated drug-drug interactions (DDIs) is investigated before marketing of a drug As early as possible to ensure safety and efficacy during clinical trials, as well as during clinical use after approval A mechanistic understanding during drug discovery aids structure-activity relationships (AR) and prediction of pharmacokinetics (PK) Front-loading specific transporter studies allows identification of transporter-related interactions prior to candidate nomination 1. Absorption increase or limit absorption 2. Disposition contribute to or inhibit distribution and/or metabolism 3. Toxicity systemic or organ specific Efficacy afety PAGE 9 FDA guidance 2012 Barton and Riley 2016
Uptake Transporters, Drug Disposition and Toxicity Effects Uptake transporters can facilitate the translocation of compounds with poor permeability across the lipoidal membrane (hydrophilic, polar, charged, large molecules) Utilise transporters to increase concentration at target site Enable the oral absorption and tissue exposure Impact on drug disposition and clearance Considerations Inhibition of uptake transporters can result in increased exposure and thus toxicity Mediated by perpetrator drugs e.g. NPs e.g. CsA (pravastatin AUC increase ~10 fold) or LCO1B1c521T>C (simvastatin AUC increase ~5 fold) PAGE 10 hitara et al., 2013 Niemi et al., 2011
Hepatic Uptake Transporters Location and function Located on the basolateral (sinusoidal) hepatocyte membrane Function in uptake of endogenous substance and xenobiotics into hepatocytes Main superfamily: OATP (organic anion transporting polypeptide) Responsible for the hepatic uptake of a broad range of endogenous and exogenous compounds Clinical importance in DDI Postulated to increase intracellular concentration increasing the probability of DILI OCTs (organic cation transporters) and OATs (organic anion transporters) are also involved in hepatic uptake of drug molecules NTCP (sodium-dependent uptake transporter) are mainly involved in bile salt uptake PAGE 11 Krajcsi et al., 2013
Hepatic Uptake The Process CL int,uptake CL int,pass CL int,pass CL int,met CL int,efflux M M M CL int,pass M CL int,efflux M M M CL int,pass CL int,uptake CL int,met CL int,efflux M passive diffusion uptake into cell intrinsic metabolic clearance efflux out of cell substrate (drug) metabolite passive process active process NB PAGE 12 Image adapted from oars et al., 2007
In Vitro Analysis Quantification Methods CL int,metabolic Hepatic metabolic clearance of the compound Limited use when uptake occurs Cells + media CL int,uptake Co ompound Concentratio on (nmol/m ml) CL int,metabolic CL int uptake Appearance of compound in hepatocyte int,uptake Labour intensive: oil spin Cells only CL int,media loss CL int,media loss Disappearance of compound from the media Limited use when P app is high Media only Time (min) PAGE 13 Gardiner and Paine. 2011
Transporters and Drug Discovery Importance Evotec Chemistry Diverse chemistry use of AEI and BDDC tools Increased incidence of transporter related uptake Crucial to implement uptake assays For many compounds IVIVE is improved with media loss CL int Compounds that scale are unaffected by using the initial rate CL int For compounds where no improvement is observed, additional or alternative metabolism routes may contribute e.g. AO/XO PAGE 14
Building innovative drug discovery alliances
IVIVE Hepatocyte metabolic CL int vs Hepatocyte Media Loss CL int 5 4 3 2 1 0 0 1 2 3 4 5 Log in vitro CL int,u (ml/min/kg) IVIVE gives confidence in understanding the clearance mechanisms and the projection of human PK & PK/PD PAGE 16
In ilico Model CL int,met /CL int,pass /CL int,uptake CL int,pass passive diffusion substrate (drug) Cl int,uptake uptake into cell M metabolite Cl int,met intrinsic metabolic clearance passive process Cl int,efflux efflux out of cell active process PAGE 17
In ilico Model Reference Compounds 1000 Atorvastatin media loss 1000 Cerivastatin media loss Atorvastatin uptake Cerivastatin uptake 100 Predicted media conc n Predicted heps conc n 100 Predicted media conc n Predicted heps conc n 10 10 1 1 0.1 0.01 0 20 40 60 80 Time (min) 0.1 0 20 40 60 80 Time (min) Model estimates the parameters involved in the active and passive uptake of compounds CL int,met /CL int,pass /CL int,uptake /K mem /fu inc PAGE 18
In ilico Model Project Compounds Passive permeability decreases the options for transporter t influence 10 Compound media loss Predicted media conc n High P app CL int,met 1 Assumptions 0.1 Net CL int,pass is consistent Efflux assumed to be negligible 0.01 Binding is at steady state 0 20 40 60 80 Time (min) PAGE 19 Nordell et al., 2011
Comparison of Assays Uptake vs Media Loss Uptake Assay Complete story: CL int,met /CL int,pass /CL int,uptake Labour intensive DDI flag Clear fit of data Media Loss Assay CL int,uptake and CL int,met only imple and suited for HT No flag for DDI if P app is high ubjective fit Understanding transporter contribution in drug discovery is key Consideration of metabolism, uptake and passive diffusion can be critical to refine IVIVE Utilise media loss to understand transporter and metabolic clearance PAGE 20
Information to aid Drug Discovery Uptake Transporters Use of in silico models Uptake in hepatocytes - Media loss - Pre-clinical species + human OATP inhibition In vivo PK study Predict human hepatic clearance - Clearance mechanism tatic DDI risk assessment IVIVE - Additional routes Advanced human PK and dose prediction In-depth DDI risk assessment -Additional routes PAGE 21 Adapted from oars et al., 2009
Acknowledgements Many thanks Dr Patrick Barton Dr arah Foley Vishal Ranglani Joanne proston Dr Rob Riley PAGE 22
Building innovative drug discovery alliances Your contact: Dr Beth Williamson Team Leader DMPK Evotec UK
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