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Improving Candidate Quality Through the Prediction of Clinical Outcome.

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Presentation on theme: "Improving Candidate Quality Through the Prediction of Clinical Outcome."— Presentation transcript:

1 Improving Candidate Quality Through the Prediction of Clinical Outcome

2 Outline of this presentation  Outline of new drug failure during clinical trials.  Predictive modelling of PK and PD in lead optimisation to improve candidate quality:  Physiologically based PK (PBPK) modelling  Physiologically based PK/PD modelling of statin effect in vivo  Summary

3 Outline of this presentation  Outline of new drug failure during clinical trials.  Predictive modelling of PK and PD in lead optimisation to improve candidate quality:  Physiologically based PK (PBPK) modelling  Physiologically based PK/PD modelling of statin effect in vivo  Summary

4 Data from: Kola & Landis 2004, Nat Rev Drug Disc 3, 711 Failures occur at all clinical stages due to lack of understanding of human in vivo pharmacology Tolerability, PK Efficacy: small population Efficacy: larger population

5 Outline of this presentation  Outline of new drug failure during clinical trials.  Predictive modelling of PK and PD in lead optimisation to improve candidate quality:  Physiologically based PK (PBPK) modelling  Physiologically based PK/PD modelling of statin effect in vivo  Summary

6 PBPK models mimic the fates of compounds in the body  PBPK models are mathematical simulation models.  They are devised to mimic the fate(s) of compound(s) in the bodies of humans, preclinical species or other organisms.  They are expressed as sets of differential equations that are solved simultaneously by computer.  Their primary output is the change over time of relevant quantities, e.g.:  the concentration of a compound in the plasma and other tissues.  the amount of a compound eliminated in the urine.  the amount of a compound absorbed from the GI tract lumen.

7 A conceptual physiological model used to predict somatic distribution and elimination Venous Blood Arterial Blood Lung Heart MuscleFat Brain Kidney Liver Stomach Intestines Capillary bed Interstitial fluid Intracellular space

8 Simulated and in vivo plasma concentrations in human biperiden acecainide dofetilide budesonide

9 Cyclosporine A: Prediction of Blood and Tissue PK in Rat Plasma/blood Adipose Muscle

10 Outline of this presentation  Outline of new drug failure during clinical trials.  Predictive modelling of PK and PD in lead optimisation to improve candidate quality:  Physiologically based PK (PBPK) modelling  Physiologically based PK/PD modelling of statin effect in vivo  Summary

11 1.By linking the prediction of PD to the prediction of PK it is possible to:  Determine the degree of accuracy that is necessary for the PK prediction.  Assess the importance of the prediction of tissue (as opposed to plasma) concentration - for those drugs that act outside the bloodstream.  Incorporate additional information (in vitro and in vivo efficacy data) for PK model development and testing. 2.Clinical success rates are low because of poor understanding of in vivo pharmacology. Prediction of in vivo efficacy will improve this situation. Why predict pharmacodynamics?

12 Integration of multiple data sources for predicting in vivo PK and efficacy Hit IdentificationLead IdentificationLead Optimisation Activity ADME Receptor binding Cellular systems Animal models Lipophilicity Solubility Metabolism Permeability Preclinical PK Preclinical animal PK Biliary clearance PK Prediction e.g. PBPK modelling Efficacy Prediction e.g. PK/PD modelling Existing drugs H uman and animal in vivo efficacy Human and animal PK

13  Statins are substrates for transporters - OATP, PGP, BCRP, etc.  This make PK prediction challenging.  Some statins are metabolised, with active metabolites.  This makes PD prediction challenging. Statins: challenging compounds for PK/PD prediction

14 Critical PK/PD properties in modelling the in vivo action of statins statin metabolite HMGCoA mevalonate cholesterol LDL cholesterol HMGCoA reductase synthesisdegradation CYP LDL cholesterol + - BILE BLOOD HEPATOCYTE

15 Sources of data for model parameters: ADME/PK PropertyCurrent sourcePotential alternative source* Plasma protein binding Equilibrium dialysis Hepatic metabolic clearance Hepatic microsome Hepatic uptake clearance Isolated hepatocyte uptake Biliary clearance1.Bile-duct cannulated rat 2.Isolated perfused rat liver 3.Sandwich cultured hepatocyte Sandwich cultured hepatocyte GI tract solubilityThermodynamic solubilityKinetic solubility GI tract permeabilityOptimisation on human plasma dataCaco-2 permeability * to facilitate model development at an earlier stage in discovery

16 Sources of data for model parameters: activity PropertyCurrent sourcePotential alternative source* In vitro efficacyPurified catalytic fragment of human HMGCoA reductase In vivo efficacyChanges in plasma LDL- cholesterol of marketed statins in humans Changes in plasma LDL- cholesterol in preclinical species † * to facilitate model development at an earlier stage in discovery † where not developing ‘me-too’ compounds

17 Optimisations of oral dose pharmacokinetics for two statins

18 Pharmacodynamic predictions: can predict reduction in plasma LDL cholesterol level

19 Pharmacodynamic predictions: can predict dose- dependency of LDL-cholesterol reduction Actual %LDL Cholesterol Reduction per Dose Predicted %LDL Cholesterol Reduction per Dose

20 Outline of this presentation  Outline of new drug failure during clinical trials.  Predictive modelling of PK and PD in lead optimisation to improve candidate quality:  Physiologically based PK (PBPK) modelling  Physiologically based PK/PD modelling of statin effect in vivo  Summary

21 Summary  Delivering candidates that will have sufficient activity in vivo is the critical goal of drug discovery.  PK and PK/PD prediction modelling are valuable tools in achieving this  PK/PD modelling approach is more powerful than just PK alone:  Can identify the compounds that should be the most efficaceous in vivo.  Simultaneously modelling data on ADME/PK and efficacy/PD enables quantitative synergy: the results are likely to be more valuable than using the datasets individually.  The same principle applies to the additional incorporation of toxicity data for the prediction of in vivo toxicity and therapeutic window.

22 Drug development is not a lottery!  Drug development has some characteristics of a lottery:  The likelihood of success is low.  The benefit of success is potentially (but not always!) very great.  But it has some characteristics that are unlike a lottery:  The cost of entry is not negligible.  We can influence the outcome (other than by buying more tickets).


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