DMPK. APRIL 27 TH 2017 Jan Neelissen Scientific Adviser Science & Technology

Transcription

1 DMPK APRIL 27 TH 2017 Jan Neelissen Scientific Adviser Science & Technology

2 What I learned is a good DMPK profile have acceptable water solubility for development be completely absorbed, preferably via passive absorption have high bioavailability (F>50%) for oral drug have a low plasma clearance CL and acceptable distribution volume have a linear kinetics, i.e. exposure proportional to dose and a clear PKPD correlation be eliminated by several pathways, and metabolized by more than one enzyme for de risking DDI have a simple metabolite profile, with no reactive metabolite have no CYP and transporter inhibition or induction have a sufficient safety margin

3 Reasons for attrition in pharmaceutical industry Modified from Boyer S et al, Chap 1: Attrition in drug discovery and development 5-45, ATTRITION IN THE PHARMACEUTICAL INDUSTRY, 2016

4 DMPK reasons for attrition in pharmaceutical industry ORAL BIOAVAILABILITY IS (STILL) A KEY FACTOR IN ATTRITION! DOSE IS LIMITED BY COMPOUND CHARACTERISTICS OR.. TISSUE EXPOSURE NOT ESTABLISHED IN OTHER WORDS: THE COMPOUND HAS NOT REACHED THE TARGET IN SUFFICIENT AMOUNTS TO GIVE EFFECT

5 How to reduce attrition due to lack of efficacy? Understand the mechanism of drug action Increase the translation of in vitro compound potency to the in vivo setting Identify PK properties for further improvement and optimal drug design or identify PK properties for best effect Improve translation of findings from preclinical species into the clinical setting Early formulation strategy In other words, implement pharmacokinetic (PK) and pharmacodynamic (PD) strategies in early research: PKPD

6 Some definitions Pharmacokinetics (PK) searches for the answer to the question: what does the body do to the drug? (DMPK, ADME) This in contrast to pharmacodynamics (PD): what does the drug do to the body? Science that describes the interplay between PK and PD: PKPD

7 How to achieve reduced attrition? 1. Partnership amongst key scientists involved Biologist Chemist DMPK scientist Modeling scientist Pharmacologist Formulation scientist 2. Define parameters that influence study designs 3. Data analysis and interpretation Applying PKPD modeling in early discovery can minimize animal usage, shorten development time, estimate the therapeutic index and predict dose ranges in early clinical testing

8 Conduct preliminary PKPD analysis 1. If possible, start with a tool or reference compound and generate a complete data package, or characterize your own compound 3. The ultimate goal is to understand the driving force(s) for response, i.e. the relationship between drug concentration and PD readouts 3. C max / MIC (minimum inhibitory concentration) 4. AUC (area under curve) / MIC 5. T(ime) > MIC 6. A combination of these parameters? C max /MIC AUC/MIC T>MIC MIC Leedahl youtube 2015

9 Conduct preliminary PKPD analysis 3. Create a PKPD model with a clearly defined dose-response relationship: 4. Typically you will not be doing in vivo PK and PD early in the project, unless you only have one or a few compounds 5. How to start with your (earliest) compounds?

10 Candidate Lead optimisation Hit-to-lead Typical ADME tests to be conducted during preclinical studies Log/7,4 (1,5-4,5) compound (1-1000) Solubility 7,4 (>1-10 µm) Solubility 6,5 (>10 µm FaSSIF) Metabolic stability Clint (h,r,d,m) Potency Enzyme/cell based assays Caco-2/MDCK permeability >1-3.10E-6 cm/s, efflux Hepatocytes Clint (h,r,d,m) In vivo PK (m,r,d,m) F>20%, Cl<50%BF, dose<100mg PPB (h,r,d,m) Fu>0,1-1% CYP inhibition Major CYP>1-10µM Met ID, CYP ID Metabolism route Blood/plasma ratio (h,r,d,m) herg IC50 CYP induction Reactive metabolite (GSH) PKPD Dose response Transporter Substrate/inhibition Non-GLP tox (rodent,dog) MTD (rodent) Dose = Ce(ss) Cl τ F CS (candidate selection) GLP tox (safety package) IND enabling (candidate) Clinical candidate (GLP) Modified from Wan H, ADMET & DMPK 1(3):19 28 (2013)

11 (Early) understanding of human PK, PD and dose 1. Absorption 2. Clearance 3. Volume of distribution 4. Bioavailability 5. Half-life Efficacious concentration PKPD formulation Duration of exposure needed How much? How often? Adapted from Obach RS, CURR OPIN DRUG DISCOV 4(1):36-44 (2001)

12 To help make better informed decisions in your drug discovery cascade for lead compounds 1. Predict human absorption following an oral dose Input minimum: in vitro permeability data. Alternatively physicochemical data of compound Output: human fraction absorbed (F a ) 2. Predict human clearance Input minimum: in vitro intrinsic clearance in microsomes Output: human clearance (Cl) and F h 3. Predict human volume of distribution Input minimum: physicochemical data of compound Output: human volume (V d )

13 To help make better informed decisions in your drug discovery cascade for lead compounds 4. Predict human oral bioavailability (F) Input: absorption and clearance Output: F = F a F g F h 5. Predict human half-life (t½) Input: clearance and volume of distribution Output: t 1/2 = ln2 Vd Cl

14 Human predicted PK profile Single point estimates: Predicted Cp time profile in human 1. Absorption 2. Clearance 3. Volume of distribution C max, t max, AUC, t ½

15 How much? Include in vitro efficacy from cell assay Adding human cell-line in vitro EC 50 or IC 50 value: 5 g IC 50 C max /MIC AUC/MIC MIC T>MIC 500 mg

16 How often? Include in vitro efficacy from cell assay Human Cp time profile and human in vitro IC 50 value: QD IC 50 BID C max /MIC AUC/MIC MIC T>MIC

17 Understanding of human PK, PD and dose You repeat this process over and over again to find your best lead compounds, or you do it with the one/few compound(s) you have During lead optimization the quality of input gets higher: More sophisticated in vitro PK and PD data In vivo animal PK data In vivo animal efficacy data Toxicology/secondary pharmacology data Formulation data Finally you should be able to select/confirm a candidate compound and set the efficacy dose in humans!

18 The PKPD process during lead optimization and candidate selection New compound simulate verify Prediction simulate to verify in clinic

19 The PKPD process when you only have 1 compound New species simulate verify Prediction simulate to verify in clinic

20 Extrapolating preclinical PKPD data to the clinic Identify the driver of efficacy e.g. Cmax, AUC Correct for species differences in target pharmacology Scale PK to humans Make human dose predictions Link human PK profile to PD

21 Ready for phase 1 clinical trials Dose escalation studies Other factors: frequency formulation food/drug interactions Pharmacokinetic assessment Safety and toxicity evaluation Determination of dose, formulation and schedule for phase 2 trials

22 C max /MIC AUC/MIC MIC EXAMPLES T>MIC Leedahl youtube 2015

23 C max /MIC Antimicrobial PKPD AUC/MIC MIC T>MIC C max /MIC Fluoroquinolones AUC/MIC Vancomycin MIC T>MIC Penicillins Cephalosporins Carbapenems Leedahl youtube 2015

24 C max /MIC Increase AUC AUC/MIC MIC T>MIC By increasing bioavailability Factors determining bioavailability 1. Biopharmaceutical factors - F a 2. Gut wall metabolism - F g 3. Hepatic metabolism - F h First-pass metabolism: pre-systemic removal of drug by the gut wall and liver Gut wall Fa Fg Portal vein Liver Fh To site of measurement Metabolism Metabolism To Faeces

25 Increase AUC Fa Gut wall Fg Portal vein Liver Fh To site of measurement Bioavailability: F = F a F g F h To Faeces Metabolism Metabolism Example 1: F = 0.80 * 0.99 * 0.30 = 0.24 or 24% Example 2: F = 0.30 * 0.99 * 0.80 = 0.24 or 24% Do you know where your bioavailability problem lies? Lets say a new formulation fixes F a to 0.90: in example 1 bioavailability becomes 27% in example 2 bioavailability becomes 71% If you only have 1 compound, you want to be example 2 If you are big pharma you fix F h with new molecules

26 C max /MIC Increase C max AUC/MIC MIC T>MIC By increasing bioavailability F = % Cmax/MEC, AUC/MEC and T>MEC increases Lower cost of goods Less variation in PK Reduced food effect 90% 60% MEC Can it be fixed with a formulation? Maybe... You increase your chances with an early formulation strategy 30%

27 C max /MIC Increase time above MEC (T>MIC) AUC/MIC MIC T>MIC Reduce clearance t 1/2 = ln2 Vd Cl But also: Higher C max /MEC and AUC/MEC MEC But what if you are stuck with one candidate? Can it be fixed? YES! Controlled Release formulation!!!

28 What I learned is a good DMPK profile - check have acceptable water solubility for development be completely absorbed, preferably via passive absorption have high bioavailability (F>50%) for oral drug have a low plasma clearance CL and acceptable distribution volume have a linear kinetics, i.e. exposure proportional to dose and a clear PKPD correlation be eliminated by several pathways, and metabolized by more than one enzyme for de risking DDI have a simple metabolite profile, with no reactive metabolite have no CYP and transporter inhibition or induction have a sufficient safety margin

29 discover more. CATALENT PHARMA SOLUTIONS 14 SCHOOLHOUSE ROAD SOMERSET, NJ