(T-088) A Computational Modeling Framework to Provide a Feasibility Assessment, Inform Competitor Differentiation Strategy, Best-in-Class Properties, and Target Selection for Biologics Targeting IBD

Fereshteh Nazari, PhD – Principal Scientist, Takeda; Jess Wu, PhD – Senior Scientist, DMPK, Takeda; Faizan Zubair, PhD – Associate Director, DMPK, Takeda; Jangir Selimkhanov, PhD – Associate Director, DMPK, Takeda; Lucia Wille, PhD – Senior Director, Blue Rock Therapeutics LP

Objective

Inflammatory bowel disease (IBD) refers to the chronic inflammation of the gastrointestinal tract and includes diseases such as Crohn’s Disease (CD) and Ulcerative Colitis (UC). Oncostatin M Receptor (OSMR) and Interlukin -11 (IL11) are two targets identified to potentially inhibit the pro-inflammatory glycoprotein 130 (g130) signaling. Experimentally defining best-in-class properties of potential therapeutic antibodies early in the program is challenging due to the large parameter space that must be assessed. Mechanistic PKPD models of target binding that integrates receptor expression and turnover rates can be used to explore optimal drug characteristics and to aid in target prioritization, competitor differentiation strategies, optimal drug properties through a computational early feasibility assessment (EFA)[1,2]. Here we apply EFA modeling to:

1) Identify differentiation strategies for an anti-OSMR antibody with a clinical competitor molecule, and benchmark requirements for a best-in-class Takeda anti-OSMR antibody.

2) Predict feasibility of inhibiting IL-11 ligand or IL-11Ra receptor to guide target selection.

Methods

A three-compartment model is used to describe the physiological plasma, tissues, and disease interstitial volumes in which antibodies distribute. Parameterization of the model involved literature search, including receptor and ligand expression (compartment specific) and turnover, and ligand-receptor affinity. Drug specific parameters and pharmacology metric such as half-life elimination, drug affinity KD, and drug format are explored in the model through sensitivity and uncertainty analysis. The models were implemented in MATLAB_R2023a – from MathWorks.

Results

In this work we identified:
1) A best-in-class anti-OSMR antibody avoids binding to soluble OSMR, and once optimized for affinity, a best-in-class anti-OSMR antibody can maximally reduce the clinical dose by 35% versus a competitor being clinically tested.


2) Inhibiting both IL-11 or IL-11Ra is a feasible approach leading to reasonable human doses but inhibiting IL-11 requires a lower dose for 90% inhibition in comparison to inhibiting IL-11Ra. Sensitivity and uncertainty analysis was used to guide the program to assessing the clinical parameters for human dose predictions.

Conclusions

Using PKPD modeling early in drug development has helped program teams make more informed decisions such as determining which form of OSMR should be targeted, whether to inhibit IL-11 or IL-11 receptor, and ultimately which experiments should be performed to develop biologics more effectively for IBD.

Citations: [1] Marcantonio DH, Matteson A, Presler M, Burke JM, Hagen DR, Hua F, Apgar JF. Early Feasibility

Assessment: A Method for Accurately Predicting Biotherapeutic Dosing to Inform Early Drug Discovery Decisions. Front Pharmacol. 2022 Jun 8;13:864768.

[2] Spinosa P, Musial-Siwek M, Presler M, Betts A, Rosentrater E, Villali J, Wille L, Zhao Y, McCaughtry T, Subramanian K, Liu H. Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer. CPT Pharmacometrics Syst Pharmacol. 2021 Mar;10(3):220-229.