Our integrated approach combines in vitro screening data, in vivo pharmacology, and computational modelling to optimize dose selection, study design, and progression strategies, reducing attrition and accelerating development timelines.

Through simulation-based approaches, we translate in vitro potency, selectivity, and safety data into actionable dosing recommendations for pharmacodynamic studies. These models predict exposure–response relationships, identify potential liabilities early, and de-risk compound progression into in vivo studies. Our PK/PD modelling establishes quantitative concentration–effect relationships, enabling the translation of preclinical data into clinically relevant exposure profiles and effective human dosing regimens. This approach supports rational decision-making in candidate selection, formulation development, and clinical trial design.

Scientists conducting permeability assays in a laboratory environment to evaluate drug transport across biological barriers for systemic exposure.

Physiologically Based Pharmacokinetic (PBPK) Modelling

PBPK modelling integrates physiological parameters, drug-specific properties, and mechanistic ADME knowledge to simulate human pharmacokinetics. These models support in vitro–in vivo extrapolation (IVIVE), prediction of systemic exposure, and first-in-human dose projections, providing a strong simulation-supported rationale for regulatory interactions and clinical planning. Our approach also evaluates drug-drug interaction risk early by simulating a compound’s impact on metabolic enzymes, such as CYPs and UGTs, and transporters, such as P-gp and BCRP. These insights help forecast clinical safety issues and strengthen regulatory submissions.

Strategic Modelling for Due Diligence

Our modelling and simulation services support strategic decision-making during in-licensing and out-licensing activities. Mechanistic analysis, PBPK modelling, and PK/PD simulations help assess compound viability and inform investment decisions with scientific rigor. This integrated modelling framework enables confident, data-driven decisions across discovery and development, supports translational success and reduces risk in early-stage drug development.

Why Choose Sygnature Discovery for Biotransformation Support?

Sygnature Discovery combines deep scientific expertise with advanced computational capabilities to deliver predictive modelling and simulation solutions that inform decision-making across drug discovery and development. Our strategies integrate in vitro and in vivo data with tools such as PBPK modelling, allometric scaling, and mechanistic simulations to ensure translational relevance.
We specialize in:

  • Translating in vivo data into actionable PK/PD insights for rational dose selection and therapeutic index estimation.
  • Optimizing dosing strategies through PBPK and allometric modelling for reliable human extrapolation.
  • Predicting human pharmacokinetics using mechanistic simulations that inform study design and clinical development planning.
  • Evaluating drug–drug interaction risks to guide regulatory strategy and submission readiness.
  • Strengthening due diligence with robust, data-driven analysis that supports candidate selection and investment decisions.

By embedding modelling and simulation within an integrated discovery framework, we reduce development risk, enable translational success and accelerate progression to the clinic.

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Dedicated DMPK for Degraders – a flexible approach for flexible molecules
Dedicated DMPK for Degraders – a flexible approach for flexible molecules
Bifunctional degraders bind and degrade proteins via ubiquitin ligase, despite breaking bRo5 rules. Translating in vitro DMPK data to in vivo performance remains a challenge. At Sygnature Discovery, we provide tailored assays and expertise to support projects involving these compounds. Read more in the poster.
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Effective drug design: insights from DMPK
Effective drug design: insights from DMPK
Modelling and simulation approaches developed under the DMPK umbrella utilising animal in vivo data and…
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