Preclinical Development

Applied BioMath’s focus in the development phase is to protect first mover advantage and help our partners develop best-in-class drugs. Because our models are mechanistic and incorporate all relevant data (in vitro, in vivo, preclinical, and clinical), we quickly determine what parameters, such as affinity, dose, and half-life are required to be competitive and best-in-class. We simulate best and worst case scenarios to determine what kind of experiments should be performed to have a better understanding of the drug candidate and reduce uncertainty in human dose predictions. This accelerates lead generation, candidate selection and best prepares for GLP toxicology studies, saving significant time and money.

Services often involve, but are not limited to:

  • Best-in-class drug property identification
  • Mechanistic PK/PD modeling
  • Knowledge gap analysis
  • Experiment prioritization and design
  • GLP toxicity study design and analysis
  • First-in-human starting dose projection
  • Noncompartmental analysis
  • Species translation
  • Technical due diligence for in-licensing opportunities
  • Portfolio prioritization
  • Disease model development
  • Combination therapy









Related Services

Early Discovery

Applied BioMath’s early discovery services help drug development teams assess the likelihood that a drug candidate can be developed for a drug target. Questions such as whether the candidate will meet the desired route of administration and dosing schedules, whether the target should be the ligand or the receptor, as well as critical Go/No-go decisions around whether to pursue the proposed target are assessed. This helps identify failures early and prioritize what experiments are necessary moving forward, ultimately reducing project timelines and cost.

Clinical Development

Applied BioMath offers support for GLP-toxicology studies through Phase 2, including pre-IND and IND reports as well as pharmacoeconomics. We help design clinical trials by determining patient selection, sample collection times, biomarker selection, and how high and what frequency of a dose is required to assess proof of clinical concept. Our models also help you accurately predict first-in-human doses.