Introduction

Early in the drug development process, in vivo studies are often run to help select molecules with appropriate ADME characteristics, generate data in efficacy models, and evaluate toxicity. Poor exposure can lead to highly variable data, and/or ambiguous study results. Use of resource and scarce API for un-interpretable study outcomes is highly inefficient, and based on poor data, a compound may be given an undeservedly low priority. Because exposure is such a critical factor for compound evaluation during lead optimization, the formulation of the compound can be a key component of the drug selection process.

Biopharmaceutical modeling is often used to estimate the fraction absorbed of a given dose delivered (Fabs). Models range from a simple algebraic calculation of maximum absorbable dose, or MAD (Johnson and Swindell, 1996), through empirically parameterized models like IDEA™ (LION Bioscience AG) to the highly sophisticated GastroPlus™ (Simulations Plus, Inc.). Each biopharmaceutics model has utility within the drug development process and in capable hands with good inputs, can yield reasonably accurate estimates of bioavailability. While these models are most often used to evaluate the impact of physical and chemical properties of drugs on fraction absorbed, some of these models may also be used to help guide formulation approaches to achieve better bioavailability.

At the lead optimization stage, one needs a simple assessment of where a drug sits in the "biopharmaceutical landscape" so that strategies for formulation options can be assessed. A model developed at the University of Michigan (Oh, et al., 1993) called the microscopic mass balance approach (abbreviated here as MiMBA) meets this criterion. Understanding the model output can help answer questions such as:

"Why am I getting poor exposure?"

"Will a better formulation help me?"

"What formulation technologies should be tried?"

Was this article helpful?

0 0

Post a comment