Driving efficient, data-driven design cycles with LiveDesign
Dispersed teams and remote work environments can pose a significant obstacle to real-time collaboration in a drug discovery program. LiveDesign, Schrödinger’s cloud-based enterprise informatics platform, enables project teams to design compounds, run predictive models, and analyze experimentally-generated data in one place, allowing teams to drive design-maketest- analyze (DMTA) cycles either independently or collaboratively in teams. Centralizing data access and design tools within LiveDesign was imperative to the project team’s ability to tackle their α4β7 integrin inhibitor design challenge (Figure 1).
Using LiveDesign, the Morphic and Schrödinger teams efficiently evaluated and triaged thousands of design ideas much faster than with traditional methods, with far fewer meetings required. Using LiveDesign as the data and predictive modeling platform allowed the teams to utilize a multi-parameter optimization (MPO) strategy driven by advanced data analytics, accurate biophysical modeling of potency and selectivity, and predictive models for pharmacokinetic (PK) properties. Enabled by seamless integration of data, visualization, and predictive modeling, the team rapidly prioritized molecules for synthesis and testing, which resulted in the identification of novel chemical matter that combined the key properties required to overcome all hurdles.
Gaining potency and balancing ADMET properties through rigorous computational assays
Through generation of over 100 proprietary co-crystal structures of inhibitor-bound α4β7 as well as α4β1 — a key off-target — the project team gained significant novel insight into the structure-activity relationships (SAR) of potency and selectivity. However, balancing PK properties with potency remained unsolved. To address this challenge the team combined advanced pKa prediction (a parameter known to correlate with oral bioavailability) using quantum mechanics (Jaguar) with free energy perturbation simulations (FEP+) for accurate potency prediction. The accuracy and utility of FEP+ as a computational assay for the prediction of relative binding energies of molecules has been validated extensively, generating predictions within 1.0 kcal/mol of experimental values on average.1
Over 8,500 FEP+ calculations were performed, enabling the team to improve potency 1000-fold, while simultaneously addressing PK liabilities. In parallel, a membrane permeability prediction model was applied to each design as part of the MPO strategy. Compounds that met the computational MPO were then synthesized in the lab. All resulting data obtained for synthesized analogs — both modeled and experimentally derived — were stored and analyzed in LiveDesign. Compounds that were identified to satisfy potency, selectivity, and permeability criteria were aggressively pursued with further characterization (Figure 2). The team’s efforts culminated in the design and delivery of MORF-057, a clinical candidate currently in Phase 2b development for treatment of ulcerative colitis (Figure 3).
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