Nonclinical Drug Development
PCDA provides integrated high-quality solutions for Nonclinical Drug Development required for the planning and execution of successful clinical trials
Many Nonclinical Development programs provide only minimal amount of data required for the initiation of Clinical Development. In contrast, an Integrated Nonclinical Development strategy also includes prediction of efficacy and adverse events in human subjects, discovery and validation of back up indications, assessment of synergism with standard of care treatment or with other drugs. All these solutions allow to streamline the clinical development by focusing on responder population, combination with synergistically acting drugs, thus massively reducing the trial costs and risk of failure.
Nonclinical Services
- Preclinical due diligence of therapeutics
- Scientific evaluation of in-license pharmaceutical candidates
- Preparation or reviewing of preclinical development plans including timelines and costs
- Design of PK,PD, biomarker and MoA studies
- CRO selection, engagement, auditing and management
- Statistic alanalysis and interpretation of preclinical studies
- Writing of preclinical study reports, Target Product Profiles, Drug Candidate Portfolios and Investigator’s Brochures
Specific services in oncology
Design and data analysis of animal studies mimicking Phase II clinical trials. Application of patient-derived xenografts (PDXs) in preclinical development of cancer therapeutics allows to assess the drug efficacy in relation to tumor characteristics including mutations, TNM, tumor grade and stage. Well-designed Mouse Clinical Trials (MCTs) with PDXs have superior translation potential over other in vivo models especially in prediction of clinical response. Moreover, MCTs may support the identification of target population, optimize the treatment schedule, assess additivity or synergism between two or more drugs, and provide molecular mode of action.
Predictive modelling for validation of drug targets or biomarkers. Tissue microarrays (TMAs) became an important tool in drug target and biomarker validation for oncological indications. Many vendors provide extensive information about tumor characteristics and patient data. New technologies allow multiplexing of IHC staining thus the amount of data is significantly increasing. PDCA applies machine learning tools for TMA data analysis to predict factors that impact drug target expression, receptor dimerization or biomarker validity.