Aug 28 th, 2017 Pierre Daublain
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1 Analyzing the Potential Root Causes of Variability of Pharmacokinetics in Preclinical Species to Inform Derisking Strategies in Discovery and Early Development Aug 28 th, 2017 Pierre Daublain
2 Outline Problem statement Methodology and approach How prevalent is PK variability? What are the risk factors for PK variability? Case studies Conclusions 2
3 Problem Statement 3
4 Tumor volume Tumor volume Problem Statement Problems associated with PK variability Efficacy studies In vivo readouts are often averaged across animals prior to representation of averaged efficacy results for each dose level. PK variability within dose groups may impact dose-response relationship, complicating interpretation of results and affecting usefulness of the studies. Vehicle Dose 1 Dose 2 Dose 3 Low PK variability vs. High PK variability Vehicle Dose 1 Dose 2 Dose 3 Time (day) Time (day) PK collection in early studies may be inadequate to enable rationalization of individual efficacy levels. 4
5 Problem Statement Problems associated with PK variability Safety studies Overlap in exposures between escalating dose groups due to variability in exposure in preclinical safety studies is a potential development issue: Lack of clearly defined dose-exposure relationship Can lead to failure to establish adequate safety margins: AUC Max tolerated AUC Safety margin Efficacious AUC Low Medium High Dose 5
6 Problem Statement Problems associated with PK variability in human: Effects on clinical study designs may include: Dosing paradigm adjustment Stricter inclusion/exclusion criteria Prohibition of concomitant P450 inhibitors Need for complex formulations or investigation on alternate routes of delivery Dosing with food PK variability may impact final drug product label. Effects on programs can be significant and lead to increased resources in development, elongated timelines and/or program termination. 6
7 Problem Statement Hypothesis: There are early indicators that can guide teams to identify and further derisk variable compounds prior to committing to pre-clinical in vivo studies Approach: Trigger analysis to: 1. Define common understanding of acceptable variability for pre-clinical studies 2. Mine historical datasets to identify parameters/properties that inform on PK variability risk in preclinical species 3. Develop mitigation strategies to derisk compounds demonstrating high risk factors for PK variability 7
8 Methodology & Approach 8
9 Data Extraction & Leveraging Spotfire as a Single Tool for Data Analysis Created a single, merged data table containing all PK/TK at Merck along with measured and calculated ADME/physicochemical property data Drug Metabolism Pharmacokinetics Individual animal PK/TK & study information ADME properties Safety Assessment Individual animal TK & study information Pharmaceutical Sciences Physicochemical properties (solubility, permeability, etc.) High-throughput/ in-silico data HT solubility, lipophilicity Predicted physchem properties 9
10 AUC AUC How Do We Define Variability? Types of variability: Intra-animal: variability in exposure from the same animal from independent measurements (same dose level, different dosing events) Inter-animal: variability in exposure between different animals in the same dose group Inter-study: variability in exposure between different studies of identical design Vs. Day X Day Y Day X Day Y High intra-animal variability Low inter-animal variability Low intra-animal variability High inter-animal variability 10
11 Percent of Datapoints How Do We Assess Variability? Quantifying variability: Of the various metrics considered, fold variability in AUC (high/low) was selected to quantify variability. Variability = AUC max / AUC min Most relevant link to design of toxicology studies for avoiding overlap Other measures such as Standard Deviation yielded identical results AUC Analyzing variability: A parameter of interest was selected (dose, solubility, etc.) Relevant buckets were defined for that parameter Distribution of PK variability categories for each bucket was then represented Dose PK variability (max AUC/min AUC) > 1 Manual FaSSIF solubility (mg/ml) 11
12 Tolerance for PK Variability Discovery Screening PK studies PK variability less likely (low doses) Higher tolerance for PK variability Multi-dose PK studies PK/PD and efficacy studies Safety studies PK variability more likely Lower tolerance for PK variability (overlap in exposures) Early Development General assessment of PK variability (max AUC/min AUC): > 5 PK variability considered to be low Variability considered acceptable in most cases Frequent dose overlapping exposure problems, particularly in safety studies Impact of PK variability very likely to be significant 12
13 How prevalent is PK variability? 13
14 Intra-Animal vs. Inter-Animal PK Variability Inter-animal variability Intra-animal variability PK variability (max AUC/min AUC) High variability cases slightly more prevalent for intra-animal exposures than for inter-animal exposures Inter-animal variability: Animals within dosing group experience the same formulation and conditions main source of variability associated with physiological differences between animals. Intra-animal variability: Reflects multiple factors including compound accumulation, saturation of clearance mechanisms, auto-induction and changes in age or disease state. 14
15 Percent of Datapoints How does PK Variability Differ across Routes of Administration? PK variability (max AUC/min AUC) > 5 Route of administration Oral exposures are most variable and are somewhat more variable than SC and IP exposures. IV exposures are the least variable. PO dosing: multiple in vivo processes governing systemic exposure including dissolution and absorption SC and IP dosing: dispersibility, local precipitation still can impact on exposures 15
16 Are there risk factors for PK variability? 16
17 Number of Datapoints How does PK Variability Differ across Species and Oral Doses? Dose 10 mg/kg Dose = mg/kg Dose > 100 mg/kg Mouse > > > 5 Rat > > > 5 Dog > > > 5 Monkey > > > 5 PK variability > > > 5 PK variability A clear trend towards increase in variability as dose increases was observed for all species. Dog is particularly susceptible to PK variability with high variability for doses > 100 mg/kg May reflect high variability in physiological parameters for that species Higher incidence of emesis may also contribute 17
18 Percent of Datapoints Lipophilicity and PK Variability PK variability (max AUC/min AUC) > 5 HPLC log D 7 Modest increase in variability with log D 7. Potentially related to correlation of lipophilicity with solubility and risk of absorption limitation for poorly soluble compounds Low variability for compounds with log D 7 0 permeability limitation may not be a strong contributor 18
19 Percent of Datapoints Solubility and PK Variability PK variability (max AUC/min AUC) > 1 Manual FaSSIF solubility (mg/ml) PK variability slightly decreases with increasing solubility, further suggesting that factors prone to impact absorption also play a role in increasing PK variability. 19
20 AUC-Dose Proportionality Preclinical Dose Number and PK Variability To further investigate the impact of solubility limitations on oral absorption and PK variability, the preclinical dose number (PDo) concept was used. Preclinical Dose Number PDo = Oral Dose Τ mg kg Compound Solubility in FaSSIF Τ mg ml Correlation with AUC-dose proportionality (dog): Dose (mg/kg) > PDo normalizes dose and solubility, thereby enabling rapid assessment of potential for solubility-limited absorption. PDo Wuelfing et al., Mol. Pharmaceutics 2015, 12 (4),
21 Percent of Datapoints Preclinical Dose Number and PK Variability PK variability (max AUC/min AUC) > PDo Use PDo instead of solubility alone further indicates PK variability to be higher when the potential for solubility-limited absorption is stronger. - Similar effects observed individually in mouse, rat, dog and monkey 21
22 Percent of Datapoints ph-dependent Solubility and PK Variability All doses > 100 mg/kg PK variability (max AUC/min AUC) > 10 ph 2 / ph 7 solubility ratio > 10 ph 2 / ph 7 solubility ratio Bell shaped relationship between PK variability and ph solubility ratio: higher variability at low ph 2 / ph 7 solubility ratios (acids) and at high ratios (bases) Effect appears slightly more pronounced at high doses. 22
23 ph-dependent Solubility and PK Variability Compounds with ph 2 solubility >> ph 7 solubility (bases) More likely to dissolve or remain in solution in the acidic environment of the stomach, prior to precipitation as ph increases in the intestinal tract Variability in gastric ph impact on dissolution as compounds move into intestine Variability in intestinal ph variability in kinetics and extent of precipitation Compounds with ph 2 solubility << ph 7 solubility (acids) More prone to precipitate/remain in solid state in stomach, prior to dissolution in the intestine Variability in gastric ph impact on precipitation as compound moves into intestine Variability in intestinal ph impact on kinetics and extent of dissolution Compounds with ph 2 solubility ~ ph 7 solubility (neutral compounds) Dissolution is a more gradual process Dissolution and/or precipitation less likely to be affected by ph Clear sensitivity for dogs may be due to high heterogeneity in gastric ph: Dog gastric ph Pfizer data (PhysChem Forum 2011) 23
24 Permeability/BCS Class and PK Variability Percent of Datapoints No clear correlation between permeability and PK variability - May reflect low incidence of cases where permeability is truly limiting absorption BCS class: All doses > 100 mg/kg PK variability (max AUC/min AUC) BCS classification BCS class BCS class Ranking of PK variability for BCS classes: I < III << II < IV Further supports that permeability has a more limited impact on variability than solubility. Formulation optimization (compound solubilization, inhibition of precipitation, etc.) can represent an effective strategy to mitigate PK variability. 24
25 Percent of Datapoints In vivo Clearance and PK Variability PK variability (AUCmax/AUCmin) RAT DOG > 100 Clearance (ml/min/kg) > 30 Increase in variability with increasing intravenous clearance - May be due to higher effect of physiological properties on compound elimination for high clearance compounds Heterogeneity in clearance across animals could not be assessed but it may also contributes to variability in exposures 25
26 Percent of Datapoints Oral Bioavailability and PK Variability PK variability (AUCmax/AUCmin) RAT DOG > > 75 Bioavailability (%F) Incidence of PK variability much lower for orally bioavailable compounds than that with low %F Again relates potential for PK variability to suboptimal bioperformance 26
27 Other Potential Root Causes of PK Variability Impact of feeding regimen: The rate at which a drug solubilizes is an important factor in determining drug absorption from the GI tract. Changes in gastric residence time, intestinal ph and composition of intestinal fluids can result in differential absorption of compounds in the fed vs. fasted state. Could not be captured in analysis but several case studies do demonstrate this impact. Impact of GI physiology: GI physiology can vary between species leading to variable absorption. Pharmacological impact of drug on GI physiology: Increase or inhibit gastric emptying and GI transit times Depending on reproducibility such an effect may also lead to increased variability 27
28 Major Risk Factors Associated with PK Variability Risk Factor Route of administration Preclinical species Dose / Solubility / Preclinical Dose Number ph-dependence of solubility / salts BCS class Bioavailability (%F) Feeding regimen oral dosing Description higher variability in dog compared to mouse, rat, and monkey, particularly at high dose High dose (> 100 mg/kg, particularly in dog) Low FaSSIF solubility ( 0.01 mg/ml) High PDo (> 10,000) ph-dependent solubility (ph 2 solubility/ph 7 solubility > 10 or 0.1) usage of basic salts BCS class II or IV low oral bioavailability (%F 10% in rat, %F 25% in dog) lack of feeding regimen control 28
29 Case studies 29
30 Plasma Concentration (µm) Case Study 1: Managing PK Variability through Formulation and Feeding Control Pharmaceutical Properties: Weak base (pka < 2), clog P = 3.4, Crystalline free form with poor aqueous solubility: H 2 O < 0.05 µg/ml, SGF < 0.05 µg/ml, FaSSIF = 1.2 µg/ml, FeSSIF = 1.8 µg/ml ph-dependent solubility: SGF/FaSSIF < 0.04 Low permeability (LLC-PK1 Papp: 7.9 x 10-6 cm/s). BCS class IV compound Compound formulated as amorphous dispersion PK issues High PK variability in dogs with secondary maxima in some animals No dose-related increase in exposure in dog safety study mg/kg Male #1 Male #2 Female #1 Female #2 SDI: 40% DL/60% HPMCP Vehicle: 0.5% MC % SLS + 5 mm HCl Dose (mg/kg) Gender AUC (µm h) C max (µm) Female Male Female Male Time (h) 30
31 Case Study 1: Managing PK Variability through Formulation and Feeding Control Hypothesis: Variability in feeding regimen such as food intake and feeding time/frequency could contribute to PK variability. Amount of feeding affects gastric emptying time significantly. Fed 10 g (n = 16) Fed 200 g (n = 16) Gastric emptying time (min) 562 +/ /- 195 Kazuko Sagawa, J. Pharm. Sci., Vol. 98, No. 7, July 2009 Feeding regimen affects bile salt secretion, and the degree of solubilization of compounds with poor aqueous solubility. Variability in gastric ph may play a role in the dissolution of compounds or of polymer in the spray-dried formulation. Strategies: Feeding regimen control, with fixed amount of food and controlled feeding time Incorporation of buffer and surfactant in the dosing vehicle Drug load reduction to 30% to prolong supersaturation of the compound in lower GI. 31
32 Plasma Concentration (µm) AUC (µm-h) Case Study 1: Managing PK Variability through Formulation and Feeding Control 2 formulations tested at 100 mg/kg: 30% and 40% (w/w) drug load in HPMCP. 30% DL formulation given in both fasted and fed state, and later dosed at 30, 100 and 300 mg/kg Results: Normal PK profile with manageable PK variability Strong positive food effect was observed. Limited impact of drug loading Dose-related increase in exposures was achieved 10 30% (w/w) dispersion; Fed 40% (w/w) dispersion; Fed 30% (w/w) dispersion; Fasted mg/kg Time (h) Dose (mg/kg) 32
33 AUC (µm.h) Case Study 2: Managing PK Variability through Pro-drugs Pharmaceutical Properties: - Zwitterion - SGF = 1.6 mg/ml; FaSSIF = mg/ml - ph dependent solubility: SGF/FaSSIF = pre-clinical safety formulation: in situ sodium salt in 10% Tween 80 PK issues: PK variability in dog safety study and low margins Intra- and inter-animal variability in dog safety studies Overlap of exposures between doses Mortality at relatively low multiple of the tolerated dose Day 1 Day 7 Day 14 Day 1 Day mg/kg 30 mg/kg 33
34 Plasma concentration (mm) Case Study 2: Managing PK Variability through Pro-drugs Hypothesis: ph-dependent solubility results in variable absorption Approach: Pro-drugs considered to improve ph-solubility profile 100 Compound B dog PK (20 mg/kg, 6 dogs) 100 Pro-drug dog PK (20 mg/kg, 6 dogs) X AUC range X AUC range Time (h) Time (h) Results: Pro-drugs have shown improved variability in dog PK studies 34
35 Conclusions - How can we use the collective dataset to provide a framework to derisk? 35
36 An Example of PK Variability Risk Process Flow > 3X variability in single dose PK Overlapping exposure in dose escalation PK Narrow margins limit ability to adjust doses to minimize overlap Low solubility Weak Acid/Base Zwitterion phsolubility profile Impact of GI physiology Prodrug feasibility/ Chemical modifications Solubility & redispersibility in GI fluid at relevant dose Increase solubilization/ minimize precipitation through phase & formulation optimization Single dose with dose escalation Multi-dose PK Feeding regimen control Unacceptable Variability Acceptable EM with no overlap Go to safety studies 36
37 Conclusions Study parameters and compound attributes considered to be significant risk factors for PK variability were identified along with potential mitigation approaches. PK variability can be a major issue for both Discovery and Development programs, and an early assessment of these risk factors as well as a derisking strategy can be effective in reducing time and resources that would otherwise be involved in the conduct of multiple in vivo studies More details available in recent publication: Mol. Pharmaceutics 2017, 14,
38 Acknowledgments Michael Altman Mark Cartwright Kung-I Feng Iain Martin Caroline McGregor Suman Mukherjee Rebecca Nofsinger Alan Northrup Robert Saklatvala Richard Tschirret-Guth 38
39 QUESTIONS 39
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