PBPK A Platform for Bridging People & Knowledge in Early Drug Development Club Phase I workshop PBPK: A new Paradigm in Drug Development Neil Miller

Transcription

1 PBPK A Platform for Bridging People & Knowledge in Early Drug Development Club Phase I workshop PBPK: A new Paradigm in Drug Development Neil Miller 27 th April 2017

2 Summary Take home messages PBPK has become an important technique (discipline) in the pharmaceutical industry as it guides drug development: Informs dug labels Helps avoid clinical trials PBPK has the potential to drive drug discovery: Mechanistically predict for humans from in silico and in vitro inputs Virtual design of compounds with appropriate PK Quality PBPK models are built when discipline experts and a PBPK scientist work together 2

3 Content A Platform for Bridging People & Knowledge in Early Drug Development Background Guiding drug development Guide = show or indicate the way New definition of PBPK Theoretical project example Driving early drug development Drive = propel or carry along by force in a specified direction and A final thought Disclaimer: The views expressed in this presentation are those of the presenter and are not those of GlaxoSmithKline 3

4 Background Definition PBPK = Physiologically Based PharmacoKinetic modeling A mathematical modelling technique for predicting the absorption, distribution, metabolism and excretion (ADME) of compounds in humans and animals using physiology and compound specific physicochemical data. 4

5 Background Brief History History: 1937 = PBPK principles were first introduced by Torsten Teorell 1977 = First article with PBPK in its title 1990 s = First application of PBPK by the FDA was in the review of tretinoin 2000 s = Availability of dedicated PBPK commercial software = Development of equations to predict tissue distribution 2016 = PBPK guidance issued by the EMA & FDA EMA FDA 5

6 Guiding drug development Popular Applications Main focus = Drug-Drug Interactions and Paediatric PBPK modelling Jamei M. Recent Advances in Development and Application of Physiologically-Based Pharmacokinetic (PBPK) Models: a Transition from Academic Curiosity to Regulatory Acceptance. Curr Pharmacol Rep. 2016; 2: Paediatrics Drug-Drug Interactions Emerging applications = Oral absorption modelling (FDA workshop May 2016) Kesisoglou F. The Role of Physiologically Based Oral Absorption Modelling in Formulation Development Under a Quality by Design Paradigm. Clinical Trials and Translational Medicine Commentaries. 2017; In Press. Coadministration of FARYDAK with drugs that elevate the gastric ph was not evaluated in vitro or in a clinical trial; however, altered panobinostat absorption was not observed in simulations using physiologically-based pharmacokinetic (PBPK) models. 6

7 Guiding drug development Impact PBPK modelling can replace clinical trials Clinical Trial = Cost savings of hundreds of thousands of Euros = 7

8 New definition of PBPK Platform for integration PBPK = Platform for Bridging People and Knowledge Quality PBPK models are built when discipline experts, data and knowledge are integrated together i.e. chemists, biologists, DMPK scientists, pharmaceutical scientists and clinical pharmacokineticists. Collaborations among preclinical scientists, formulators, pharmacometricians, clinical pharmacologists, clinicians, and statisticians are essential for impactful and successful application as well as implementation of PBPK modeling and simulation in drug discovery and development. Jones H et al. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin Pharmacol Ther. 2015;97(3): Neil Miller April 2017 A Platform for Bridging People & Knowledge in Early Drug Development.pptx 8

9 Theoretical project example Models are a complex dynamic interplay of processes Scenario: A project team are working on new chemical entity and they require human PK predictions based on pre-clinical in vitro and in vivo data Compound characteristics... Basic: pka = 8.5 Moderate lipophilicity: logp = 3 Reasonable solubility: Aqueous solubility = 100 µg/ml Good permeability: Peff = 1 x 10-4 cm/s Low clearance: CL P = 5 L/h Moderate volume of distribution: V c = 1 L/kg B/P = 0.9 Fup = 15% 9

10 Theoretical project example Initial model is built by a biopharmaceutical scientist Focus is on achieving a 100% Fa (fraction absorbed) with a short <2h t max (time of maximum plasma concentration) following oral dosing Fa ~ 100% t max = 3.6h Conclusion: We have some work to do to speed up the t max 10

11 Theoretical project example DMPK colleague employs a systemic PBPK model The distribution of bases can be driven by the interaction with acidic phospholipids So a systemic PBPK model is employed using the Lukacova Kp method Fa ~ 100% t max = 1.68h Conclusion: We are on track to meet the target product profile 11

12 Theoretical project example Don t forget the biologist, toxicologist and PBPK scientist We may be on track with regards to Fa and t max, but concentrations drive effects and if the biologist suggests that we need to be above 0.4µg/mL at 1h or above 0.2µg/mL at 24h then both profiles are adequate But the higher C max of the One Comp PK profile may have safety implications when you share the predictions with the project toxicologist The PBPK scientist assessing the predicted tissue concentrations may warn of potential tissue accumulation on repeat dosing Muscle Adipose 12

13 Dynamic interplay of processes Quality models built by teams PBPK models are a complex dynamic interplay of processes that involve numerous scientific disciplines No one can be an expert in all of the disciplines and the PBPK software A team is required for quality PBPK model building, understanding and interpretation 13

14 Driving early drug development How early can PBPK be employed? Human PK predictions are a critical component of compound progression and clinical dose predictions How early in drug development could PBPK be employed to add value? Candidate Selection Panel Candidate Selection? Lead Optimization? Compound design? 14

15 Driving early drug development Candidate Selection (CS) Simple empirical PK prediction approaches have proved popular at CS What if dissolution and supersaturation are complex? What if distribution is multi-phasic, species specific and variable?...then PBPK is the only option for realistic human PK predictions 15

16 Driving early drug development Candidate Selection (CS) MOAM = Mechanistic Oral Absorption Model Compound X: Distribution = Multi-phasic & species specific (rat minipig) Permeability = Low/moderate & ph dependent (MDCK Papp 6-fold drop ph ) Strategy: Understand distribution in pre-clinical species using PBPK model Understand absorption in pre-clinical species using MOAM Predict human PK using MOAM + PBPK model Impact = PBPK model directed DMPK & enabled mechanistic dose prediction Mechanistic explanation for species differences in distribution Mechanistic absorption model accounting for ph dependent absorption All animal studies were ethically reviewed and carried out in accordance with Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare and Treatment of Animals. The human biological samples were sourced ethically and their research use was in accord with the terms of the informed consents Neil Miller April 2017 A Platform for Bridging People & Knowledge in Early Drug Development.pptx 16

17 Driving early drug development Candidate Selection (CS) Compound Y: Solubility = Very low in FaSSIF and FeSSIF, but high in SGF Precipitation = In vitro precipitation slow Strategy: Integrate data in a mechanistic oral absorption model (MOAM) Confirm MOAM predicts effect of solubility & particle size in dog Impact = Comprehensive risk mitigation package at Candidate Selection Combined pharmaceutics data with physiology to simulate the effect of supersaturation and precipitation PBPK suggested efficacious concentrations are achievable, but highlighted particle size reduction may be required in patients with increased stomach ph Neil Miller April 2017 A Platform for Bridging People & Knowledge in Early Drug Development.pptx MOAM = Mechanistic Oral Absorption Model Predict human PK and potential variability using MOAM All animal studies were ethically reviewed and carried out in accordance with Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare and Treatment of Animals. The human biological samples were sourced ethically and their research use was in accord with the terms of the informed consents 17

18 Driving early drug development Lead Optimization (LO) During early LO there may be numerous potential compound series Taking everything into consideration which way should you turn in the fog? PBPK allows you to account for the dynamic interplay in parameters and point you in the right direction: 18

19 Driving early drug development Lead Optimization (LO) Evaluation of the GastroPlus Advanced Compartmental and Transit (ACAT) Model in Early Discovery 623 compounds Compound properties were defined by in silico, in vitro and limited PK studies Predicted PO PK in rat and dog (C max, AUC last and T max ) Based on variability in the PK data predictions within 3-fold were considered acceptable Comparison of predicted and observed dose-normalized C max (a) and AUC last (b) in rats (N=537 observations) 19

20 Driving early drug development Lead Optimization (LO) Evaluation of the GastroPlus Advanced Compartmental and Transit (ACAT) Model in Early Discovery The generic ACAT model provided reasonable predictions (especially for BCS Class 1 compounds) despite relying merely on some basic assumptions and the use of in silico or in vitro data from high-throughput screening combined with IV PK data The model can be used to prioritise, test hypothesis and direct experimentation 20

21 Driving early drug development Compound design Could PBPK be used to design new compounds with appropriate ADMET properties from structure? Why should we believe that it is possible to predict the multiple complex interactions that drive oral bioavailability from structure? 21

22 Driving early drug development Compound design Physiologically Based Pharmacokinetic Modeling and Simulation for Drug Candidate Optimization and Selection: 62 compounds with their major reported clearance pathways all CYP450 mediated Oral bioavailability ranged from 3-99%, average = 60% Bioavailability predicted from 2D structure using PBPK 69% of the compounds had predicted bioavailability within 2-fold of reported values Assessed the prediction of a plasma concentration profile from structure for Risperidone The simulated area under the curve was within 10 percent of the observed in vivo value, and both the C max and T max simulations were within twofold of the observed in vivo values. 22

23 Driving early drug development Compound design Physiologically Based Pharmacokinetic Modeling and Simulation for Drug Candidate Optimization and Selection: The use of PBPK-based simulations in early discovery enables pharmaceutical scientists to derive greater benefit from initially sparse datasets, identify risk factors, and prioritize appropriate follow-up studies 23

24 Driving early drug development Compound design There are challenges to designing compounds using PBPK e.g. Accurate predictions of clearance Accurate predictions of tissue partitioning Mechanistic predictions of supersaturation and precipitation Accurate predictions of transporter mediated disposition If we industrialize what we know then we can find out what we don t know: PBPK Platform on Demand 24

25 Driving early drug development Impact If PBPK modelling can be used to design new prescription drugs = = Cost savings of billions of Euros Cost savings of hundreds of thousands of Euros 25

26 Summary Take home messages PBPK has become an important technique (discipline) in the pharmaceutical industry as it guides drug development: Informs dug labels Helps avoid clinical trials PBPK has the potential to drive drug discovery: Mechanistically predict for humans from in silico and in vitro inputs Virtual design of compounds with appropriate PK Quality PBPK models are built when discipline experts and a PBPK scientist work together 26

27 A final thought Based on science/automation: Why would you not use PBPK in drug development? Science: numerous disciplines (mechanisms/parameters) drive PK following oral dosing Particle Size Particle Shape Solubility Bile Salt Effect Precipitation Permeability Absorption GI Tract ph Transit Time Volume Surface Area Bile Conc. Pore Size Bioavailability/Clearance Transporter kinetics Enzyme kinetics Automation: a simple GastroPlus PBPK model with 200+ inputs (based on structure and physiology) can be built in minutes logp pka SF Reference Solubility Mean Precipitation Time Diff. Coeff. Density Particle size Shape Factor Peff Bile Salt Effect SR B/P Fup Liver CL Stomach (ph, TT, Volume) GI Physiology = 8 inputs x 8 compartments = 64 PBPK Model = 10 inputs x 13 tissues = 130 (ASF, ph, TT, Volume, SEF, Bile Salt, Pore R, Poros/L) (Kp, Volume, Blood Flow, Fut, Vnt, Vpht, Vwt, Capt, Fvec, Density) So in drug development, why would you use a simple assumption/empirical approach driven by 4 inputs and not an interdisciplinary mechanistic PBPK model? Neil Miller April 2017 A Platform for Bridging People & Knowledge in Early Drug Development.pptx Transporters: Location Abundance Enzymes: Location Abundance logp pka Fup B/P Distribution Tissue: Size Composition Blood flow Assumed fixed absorption rate Estimated bioavailability Assumed one compartment distribution Clearance 27

28 Acknowledgements I could not have done it on my own A big thank you to numerous scientists within GlaxoSmithKline: 28

29 Back up slides Extra information 29

30 Mechanistic Oral Absorption Model (MOAM) Advanced Compartmental Absorption and Transit (ACAT) model One GI compartment Whole GI tract Images from Simulations Plus 30

31 Systemic PBPK model Accounts for tissues and blood flows Whole Body One tissue Images from Simulations Plus 31

32 Useful references Good sources of information on PBPK not specifically referenced Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol. 2011;51: Poggesi I, Snoeys J, Van Peer A. The successes and failures of physiologically based pharmacokinetic modeling: there is room for improvement. Expert Opin Drug Metab Toxicol. 2014;10: