Confidence in the Clinic
Proactively planning appropriate clinical pharmacology studies or analyses to inform the label language ensures development proceeds smoothly and facilitates regulatory approval. We provide services to support your team, such as:
- Non-compartmental Analyses (NCA)
- Regulatory Support
- Pediatric Development Plans
- Clinical Pharmacology Plans & Gap Analysis
Non-compartmental analyses (NCA) are a quick, straightforward way to characterize the pharmacokinetic properties of new drugs. They provide useful information that can be used to inform drug development decisions, particularly when those decisions need to be made on very short timelines, like during dose escalation in a first-in-human trial. PK parameters derived from NCA are easy to understand and are generally reported in clinical study reports and in the label for drugs that have achieved regulatory approval. They are also conducted for preclinical PK studies, and provide the basis for calculating exposure margins used to support FIH dose selection. We can provide high-quality NCA analyses of preclinical or clinical PK data on timelines that meet the needs of your drug development program.
Clinical pharmacologists are a critical member of project development teams. Their impact is widespread, from helping to design clinical trials to selecting appropriate doses and regimens, choosing sampling schemes that manage practical considerations and scientific needs, developing strategies to translate preclinical data to inform FIH starting dose selection and predict human efficacious doses, establishing clinical pharmacology plans, managing the impact of formulation changes, contributing to the preparation of regulatory documents and interacting with regulatory agencies in response to queries.
At Applied BioMath, we can provide Clinical Pharmacology support that covers all these needs in addition to providing support from modeling and simulation scientists to inform drug development decisions.
Our scientists are experienced in working on cross-functional teams within matrixed organizations, and can translate complex technical work for a diverse audience, facilitating quantitatively driven decision-making. Our strategic support combined with our technical expertise provides for all your clinical pharmacology and modeling needs.
Preparation of Regulatory Documents
Pediatric Development Plans
Clinical trials involving pediatric patients are required for almost all new drugs in development. There are multiple challenges to overcome during pediatric drug development, from the selection of an appropriate dose in these patients to problems collecting sufficient samples to characterize PK and PD due to limitations in the blood volumes that can be withdrawn from these patients. Population modeling approaches can help overcome these challenges. After appropriate scaling to compensate for the smaller size of pediatric patients and application of functions to account for incomplete maturation, particularly in the youngest of children, doses that result in exposures matching those in adults can be selected in an informed way.
Exposure-response models developed in adults can then be used to link exposures in pediatrics to outcomes, with adjustments made for differences in disease pathophysiology in children compared to adults. Tiered fixed and fixed dosing regimens can be explored, and optimal sampling methods can be applied to determine sparse sampling schemes that ensure appropriate information is obtained to characterize PKPD in children and adolescents. Regulatory agencies expect such analyses to support pediatric drug development, and internal decision-making can be improved by the use of such approaches.
Clinical Pharmacology Plans & Gap Analysis
We understand there are many factors to consider when developing a clinical pharmacology plan. We can help you answer questions such as:
- Do we need to run a dedicated hepatic or renal impairment study
- What is the potential for drug-drug interaction and how should we manage potential interactions?
- Can we use population modeling to address clinical pharmacology questions instead of running a dedicated study?
- What studies should we run to ensure future studies have more inclusive inclusion/exclusion criteria and maximize potential enrollment?