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Discovery PBPK: Efficiently using machine learning & PBPK modeling to drive lead selection & optimization

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Presented by Viera Lukacova, Director - Simulation Sciences

Published in: Health & Medicine
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Discovery PBPK: Efficiently using machine learning & PBPK modeling to drive lead selection & optimization

  1. 1. Discovery PBPK: Efficiently using machine learning & PBPK modeling to drive lead selection & optimization Viera Lukacova Director – Simulation Sciences viera@simulations-plus.com 1
  2. 2. • About 60-80% of animal studies conducted during Lead ID/Opt stages Current Drug Discovery & Development Process Thomas et al, ATLA 2010, 38, Supplement 1, 81–85 Lead ID and Lead Opt together contribute ~32-35% towards the total cost 2
  3. 3. Say “I do” to the Machine Learning/PBPK marriage! Permeability, solubility vs. pH, pKa(s), logD vs. pH, Fup, blood:plasma ratio, tissue Kps, CLint, CLfilt 3
  4. 4. 4 The Machine Learning/PBPK Marriage: Proof of concept Using in silico machine learning & PBPK, Pfizer was interested in predicting Cmax, AUC, and the PK profile in human for their PF-03084014 compound Hosea et al., Mol Pharm. 2013 Apr 1;10(4):1207-15 4
  5. 5. Hosea et al., Mol Pharm (2013) • So it worked on 1 compound – how about others? The Machine Learning/PBPK Marriage: Proof of concept (cont.) 5
  6. 6. Novartis Computational Chemistry Group – Using ‘Discovery PBPK’ to Guide Lead Optimization Daga et al. (2018) Mol. Pharmaceutics 15(3):821 Daga et al. (2018) Mol. Pharmaceutics 15(3):831 6
  7. 7. • Trouble with clearance can be overcome by building a NCA CL model using a small number of compounds (CLloc) • 49 Compounds: Single congeneric series reported by Merck in various papers – RAT in vivo data: %F, CLp Case Study #1: Dipeptidyl Peptidase-4 Inhibitors CLglbExp CLp CLloc Daga et al. (2018) Mol. Pharmaceutics 15(3):821 Daga et al. (2018) Mol. Pharmaceutics 15(3):831 7
  8. 8. • Hepatocyte CL provides accurate estimate of CL and hence %F • 81 Compounds: Single congeneric series reported by AstraZeneca in 4 papers – RAT in vivo data: %F, CLp – in vitro data: CLint(hep) Case Study #2: 11-HSD1 Inhibitors CLglbExp Hep CLintExp CLp CLloc Daga et al. (2018) Mol. Pharmaceutics 15(3):821 Daga et al. (2018) Mol. Pharmaceutics 15(3):831 8
  9. 9. • 61 compounds : Single congeneric series with experimental data – In silico & in vitro physicochemical data: (solubility, Caco-2 permeability, plasma protein binding, CLint) – Rat PK data (%F, AUC, Cmax, Tmax, CLplasma, Vss) Case Study #3: Internal Kinase-“X” Inhibitor series in silico prop & CLglbExp input prop & CLintExp CLp in silico prop & CLloc Daga et al. (2018) Mol. Pharmaceutics 15(3):821 Daga et al. (2018) Mol. Pharmaceutics 15(3):831 9
  10. 10. Increasing training data size, improved performance Local Models OK w/ only ~15 Rat Data Points DPP-4 48 cpd HSD1 81 cpd Kinase-X 63 cpd Global Local ~35Local ~15 Local ~50 Local ~70 >75% of compound predicted within 3-fold from chemical structure Daga et al. (2018) Mol. Pharmaceutics 15(3):821 Daga et al. (2018) Mol. Pharmaceutics 15(3):831 10
  11. 11. HTPK: Conducting PK simulations at high speed 11
  12. 12. %Fa %Fb Vd CLsys * Modified from van de Waterbeemd, H, and Gifford, E. Nat. Rev. Drug Disc. 2003, 2:192-204 HTPK Simulation Module [HTPK = High Throughput PharmacoKinetic] ACAT™ Model Fraczkiewicz, AAPS 2018, Rapid Fire presentation 12
  13. 13. HTPK Simulation Module – Inputs Input required for %Fa and %Fb Corresponding ADMET Predictor model Diffusivity in water DiffCoef Solubility in water S+Sw Salt solubility factor SolFactor Solubility in fasted intestinal fluid S+FaSSIF Solubility in fed intestinal fluid S+FeSSIF pKa S+pKa Human jejunal permeability S+Peff log P S+logP Volume of distribution Vd Percent unbound in plasma hum_fup% Microsomal fraction unbound S+fumic Metabolic clearance CYP_HLM_CLint Blood to plasma ratio RBP Precipitation time <user input> Dose <user input> Dose volume <user input> Particle radius <user input> Body weight <user input> * Any predicted input can be replaced by experimental values Fraczkiewicz, AAPS 2018, Rapid Fire presentation 13
  14. 14. HTPK Simulation Module – Outputs Outputs generated by the %Fa/%Fb model Fraction absorbed Fraction bioavailable Cp(t) = plasma concentration vs. time profile C_max = maximum plasma concentration t_max = time of peak plasma concentration AUC = area under the Cp(t) curve Sensitivity to permeability and solubility Mechanistic estimation of the volume of distribution Outputs generated by the OptDose model Optimal dose to reach desired plasma concentration at steady state Fraczkiewicz, AAPS 2018, Rapid Fire presentation 14
  15. 15. • Gut clearance is not considered • Passive absorption kinetics only • Clearance is assumed to follow linear kinetics • Metabolism models only consider CYP enzymes • Enterohepatic circulation and biliary excretion not considered • First-order precipitation kinetics assumed • Dosage form defined as IR tablet • Physiology set to either fasted human (adult) or rat HTPK Simulation Module – Limitations Fraczkiewicz, AAPS 2018, Rapid Fire presentation 15
  16. 16. 90% predicted within 2-fold of the observed value. 83% predicted within 1.5-fold Performance Snapshot: %Fa 125 passively-absorbed compounds from Zhao [Zhao et al. J. Pharm. Sci., 2001, 90: 749.] ReportedFa%%Fa(observed) %Fa (predicted) Some outliers are predicted to be actively transported Fraczkiewicz, AAPS 2018, Rapid Fire presentation 16
  17. 17. 81% predicted within 2-fold of the observed value. ~70% predicted within 1.5-fold Performance Snapshot: %Fb 62 CYP-metabolized compounds from Toshimoto data set [Toshimoto et al. Drug Metab. Dispos. 2014, 42:1811.] ReportedFb%%Fb(observed) %Fb (predicted) Some outliers are predicted to be UGT/esterase substrates Fraczkiewicz, AAPS 2018, Rapid Fire presentation 17
  18. 18. HTPK Simulation Module – Performance • Calculation of fraction absorbed (%Fa) and fraction bioavailable (%Fb) in human after 24 h at three different dose levels: 1 mg, 10 mg, and 100 mg. • 2284 diverse drugs extracted from World Drug Index Platform (*) Processing time for 2284 drugs Laptop A 3.9 min (~0.1 s/drug) Laptop B 2.5 min (~0.06 s/drug) (*) • A = DELL XPS with Intel® Core™ i7- 3537U CPU 2.5 GHz, 8 GB RAM, 64- bit, running Windows 7. • B = ASUS R.O.G. with Intel® Core™ i7-7700HQ CPU 2.8 GHz, 16 GB RAM, 64-bit, running Windows 10. Fraczkiewicz, AAPS 2018, Rapid Fire presentation 18
  19. 19. • Combination of accurate QSAR and PBPK models shows potential of using in silico approaches in drug discovery settings • Simplified models may be applied to screen large compound libraries • The main limitations of the in silico approach: – Quantitative clearance prediction • Local models built on small sets of compounds were shown as possible solution to improve the in vivo predictions – Quantitative prediction of drug interactions with specific enzymes and transporters • Availability of experimental data is the limiting factor to building the in silico models • There are still gaps in the physiological information (expression levels in different tissues) Summary 19
  20. 20. Clearance Classification Models El-Kattan AF, Varma MVS; Drug Metab Dispos 46: 729-739 • Three binary (Yes/No) models for Metabolism, Renal and Hepatic Uptake • 3-class ANNE model • Risk model based on rules from El- Kattan and Varma 20
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