Download Site-of-Action (SoA) Models to Predict Tissue Target Coverage in

Site-of-Action (SoA) Models to Predict
Tissue Target Coverage in Humans
AAPS NBC Meeting 2016
Anson K. Abraham, Ph.D.
16-May-2016
(Work Performed at Pfizer Inc., Currently at Merck & Co. Inc.)
Acknowledgements:
Pfizer: Abhinav Tiwari, Pratap Singh, Indranil Bhattacharya, Xiaoying Chen,
Hannah Jones, & Anup Zutshi;
RES Group: Haobin Luo, Paul Jasper, John Tolsma
Outline • Minimal Physiological Models: Brief Introduc8on to Site-­‐of-­‐Ac8on Models • Predic8ng Tissue Target Coverage ü Importance of Tissue Data
• Conclusions & Recommenda8ons Minimal Models A0emp2ng to Characterize Data: PK-­‐biomarker-­‐symptom model •  Model predic8ons show a difference in the cons8tu8ve produc8on rates of IL-­‐1B in healthy (6 ng/day) vs CAPS disease (25 ng/day) ²  Modeled as a posi8ve feedback loop •  The proposed model was used to demonstrate a reduc8on in the produc8on rate of IL-­‐1B to normal levels •  Free IL-­‐1B predicted in both, central and peripheral compartments © 2009 Lachmann et al.
Helen J. Lachmann et al. J Exp Med 2009;206:1029-­‐1036 Peripheral Binding Model with Increasing Complexity: Applica2on to Bispecific An2bodies •  Bispecific an8bodies can bind to two targets •  Sequen8al binding to 2-­‐targets at 2 sites (plasma & 8ssue) •  Targets: Membrane-­‐bound (ICOSL) and soluble (CXCL13) Ø  Target site dynamics •  Less rapid and shorter dura8on of free target suppression compared to plasma •  Increasing affinity affects target binding in 8ssues with minimal impact in plasma Chudasama et al. JPKPD, February 2015, Volume 42, Issue 1, pp 1-­‐18 Key Ques2ons
Ø Can M&S help predict target coverage in disease 8ssue? ü Based on previous examples, seems to be “YES” Ø What level of confidence does one have on the model predicted 8ssue target coverage? ü  Will lack of baseline 8ssue data result in high uncertainity pertaining to 8ssue target coverage predic8ons? •  Case study for a specific SoA model Ø What is the minimal data requirement to address any uncertainity in model predic8ons? ü M&S workflow results (specific SoA model) Peripheral Binding “Site-­‐of-­‐Ac2on” Model for Soluble Targets Structural Model Assump2ons: •  Drug assumed to be a large molecule that is primarily restricted to the inters88um (e.g: mAb) •  Target is soluble and can freely distribute between plasma and SoA •  Target is synthesized in SoA, but is degraded both in plasma and SoA •  Drug and complex are eliminated systemically •  Complex distribu8on rates are same as drug Modeling Workflow Serum/Plasma Parameter Set •  A total of 32 parameter combina8ons were evaluated •  Five model parameters (𝐾𝐷, 𝑇𝑃0, 𝑅𝑎𝑡𝑖𝑜𝑇, 𝑡ℎ𝑎𝑙𝑓𝑇𝑝, and 𝑡ℎ𝑎𝑙𝑓𝑇𝑠) were set to a low or high value to arrive at 32 (25) parameter sets such that: •  𝑇𝑃0<𝐾𝐷 ≤ 𝑇𝑆0 (case 1-­‐12; n = 12), •  𝐾𝐷<𝑇𝑃0<𝑇𝑆0 (cases 13-­‐20; n = 8), and •  𝑇𝑃0<𝑇𝑆0<𝐾𝐷 (cases 21-­‐32; n = 12) Modeling Workflow Serum/Plasma Increasing Data Points in Tissue Tissue Modeling Workflow Increasing Data Points in Tissue Ts
Target Coverage = 1−
Ts,0
Tissue An2cipated Outcomes LOW Uncertainty in Tissue Coverage Predic2ons Availability of Tissue Target Data Knowledge of System Parameter Sensi2vity An2cipated Outcomes LOW Uncertainty in Tissue Coverage Predic2ons Availability of Tissue Target Data Knowledge of System Parameter Sensi2vity HIGH Uncertainty in Tissue Coverage Predic2ons Uncertainty in Target Coverage Predic2ons Decreases with Addi2onal Tissue Data •  Heat maps depict the uncertainty in target coverage for 1 mg/kg (leh) and 10 mg/kg (right) IV doses at the end of treatment (56 days) •  Uncertainty in target coverage predic8ons dropped significantly with the inclusion of one 8ssue data point •  In all the cases analyzed, inclusion of two 8ssue data points resulted in less than 15% uncertainty 1
Sensi2vity Analysis – Assessing Sensi2vity of Target Coverage to Model Parameters Two different global sensitivity analysis (GSA) methods to examine
the sensitivity of target coverage to various model parameters
•  Baseline (Ra%oT, Tp0), binding (KD), and turnover parameters (thalfTs and thalfTp) scored as sensi8ve (at least by one method) •  Explore fixing parameters that may be determined experimentally –  Fix KD, Ra%oT, thalfTp, Tp0 Address Uncertainty in Tissue Target Coverage by Fixing Parameters Fixed KD
Fixed Ra%oT
• Fixing KD significantly reduces coverage uncertainty v Determined through methods such as Biacore or Kinexa v Does in vitro KD translate to in vivo KD Address Uncertainty in Tissue Target Coverage by Fixing Parameters Fixed thalfTp
Fixed TP0
• Fixing thalfTp significantly reduces coverage uncertainty v Pulse chase labeling to determine in vivo turnover of targets Conclusions Ø  Minimal physiological models are s8ll complex enough to warrant in depth analysis ü  Characterize uncertainty of target coverage predic8ons at the site of ac8on ü  Define key parameters that significantly influence their predic8ons Ø  Resourcing measurement of baseline target concentra8on in 8ssue (possibly end of study as well) via biopsy samples is important to reduce uncertainty in target coverage predic8ons Ø  Target-­‐related parameters in the presented SoA model, target half-­‐life in the site of ac8on (thalfTs) and baseline target concentra8on in plasma (Tp0), considerably impacted target coverage predic8ons Ø  The workflow developed here serves as a useful guide to systema8cally understand uncertainty in target coverage predic8ons Backup Peripheral Binding Model with Increasing Complexity: Applica2on to Bispecific An2bodies •  Model for Bab against IL4, IL-­‐13, and IL-­‐13 8ssue receptor Chudasama et al. JPKPD, February 2015, Volume 42, Issue 1, pp 1-­‐18 Minimal Models A0emp2ng to Characterize PK of mAbs – mPBPK •  Target mediated binding of mAb in plasma (cTMDD) or inters88al fluid (pTMDD) “Assigning TMDD consistent with target-­‐expressing %ssues is important to obtain reliable characteriza%ons of receptors and receptor binding” Cao, Y., & Jusko, WJ., JPKPD,August 2014, Volume 41, Issue 4, pp 375-­‐387