Pharmacometrics in Drug Development

Pharmacometrics in Drug Development

Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactions between drugs and patients. This involves Pharmacokinetics (PK), Pharmacodynamics (PD) and disease progression with a focus on populations and variability.

Pharmacometrics is emerging as a science that quantifies drug, disease and trial information to support efficient drug development and regulatory decisions. Drug models describe the relationship between exposure (PK), response (PD) for both desired and undesired effects, and individual patient characteristics.

Disease models describe the relationship between biomarkers and clinical outcomes, time course of disease and placebo effects. The trial models describe the inclusion/exclusion criteria, patient discontinuation and adherence. Typical focus of Pharmacometrics has been on drug models, also referred to by terms such as: concentration-effect, dose-response, PKPD relationships.

Dose Response Relationship

The PK/PD and/or dose-response relationship describes the change in effect (response) on individuals caused by differing levels of exposure (or doses) after a certain exposure time. Studying dose response, and developing dose response models, is central to determining “safe” and “hazardous” levels and dosages for drugs. This information can help identify an appropriate starting dose, the best way to adjust dosage to the needs of a particular patient, and a dose beyond which increases would be unlikely to provide added benefit or would produce unacceptable side effects. Dose-concentration, concentration- and/or dose-response information are used to prepare dosage and administration instructions in product labeling. In general, useful dose-response information is best obtained from trials specifically designed to compare several doses. Conducting dose-response studies at an early stage of clinical development may reduce the number of failed Phase 3 trials, speeding the drug development process and conserving development resources. It is important to choose as wide a range of doses as is practical and safe for patients to evaluate clinically. A widely used, successful, and acceptable design, but not the only study design for obtaining population average dose-response data is the randomized parallel, dose-response study with three or more dosage levels, one of which may be zero (placebo). From such a trial, if dose levels are well chosen, the relationship of drug dosage, or drug concentration, to clinical beneficial or undesirable effects can be defined. Note that a single dose level of drug versus placebo allows a test of the null hypothesis of no difference between drug and placebo, but cannot define accurately the dose-response relationship.

FDA perspective of Pharmacometrics

The Pharmacometric division at the FDA reviews a variety of Pharmacometric analyses types including population PK, exposure-response (or PK/PD), biomarker-clinical outcome modeling, and simulations to determine optimal dosing based on benefit-risk assessment and therefore it has been emphasized for the need for early interaction between the FDA and sponsors to plan the development more efficiently. FDA review was performed in 2006. They surveyed fiscal years 1995 and 1996 and found 23% of NDA submissions contained population pharmacokinetics and/or pharmacodynamic reports(1). The use of the population approach (population pharmacokinetics/ pharmacodynamics) provided useful information for the drug label in 83% of the 47 submissions on safety, efficacy and dosage optimization. However, in 17% of the 47 applications, the use of the approach did not yield any positive impact because the population approach was not integrated into the original plan of the drug development program.

Another review was performed by V. A. Bhattaram where he assessed the role of Pharmacometrics in making drug approval and labeling decisions. Cardio-renal, Oncology, and Neuropharmacology drug products divisions were surveyed from 2000 to 2004. About 20% of the studies included a pharmacometric component from which about 50% had their Pharmacometric analyses being pivotal in regulatory decision making.

The overall conclusion was that Population pharmacokinetics should, therefore, be integrated into drug development.

When should we perform Pharmacometric Analysis?

Pharmacometric analyses are designed, conducted and presented in the context of drug development, therapeutic and regulatory decisions. The single-most important strength of such analyses is its ability to integrate knowledge across the entire development program and compounds, and biology. In drug development, the population approach can help increase knowledge of the quantitative relationships between drug input patterns, patient characteristics, drug disposition, and responses. The population approach may be used to estimate population parameters of a response surface model in phases 1 and 2B of clinical drug development, where information is gathered on how the drug will be used in subsequent stages of drug development and after release (through the use of estimated population dose response relationship). The population approach may increase the efficiency and specificity of drug development by suggesting more informative designs and analyses of experiments. The population approach can also be applied to phases 2A and 3 of drug development to gain information on drug safety (efficacy) and to gather additional information on drug pharmacokinetics (and pharmacodynamics) in special populations, such as the elderly. It is also useful in post-marketing surveillance (phase 4) studies.

Pharmacoemtrics at Bioforum

Bioforum provides Pharmacometrics services to projects of preclinical, early clinical Phase and late clinical phase product development. These include Pharmacokinetics (PK), Pharmacodynamics (PD) and disease progression with a focus on populations and variability.  

The Pharmacometric group works in parallel with multi-disciplinary Bioforum clinical development teams, specifically biostatistics, clinical data management and medical writing aimed at producing the highest-quality and informative documents.

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