How Much API Is Too Small for High Throughput Screening

Thursday, November 12, 2009: 9:35 AM
Jackson E (Gaylord Opryland Hotel)

Shella L. Tameze, Analytical Research and Development, Solid State Characterization Group, Bristol-Myers Squibb, New Brunswick, NJ
Victor Rosso, Pr&d, Bristol-Myers Squibb, New Brunswick, NJ
Anisha Patel, Ar&d, Bristol-Myers Squibb, New Brunswick, NJ
Beth Sarsfield, Ar&d, Bristol-Myers Squibb, New Brunswick, NJ
Shawn Yin, Ar&d, Bristol-Myers Squibb, New Brunswick, NJ
Boris Shekunov, Ar&d, Bristol-Myers Squibb, New Brunswick, NJ

Crystallization from solution is by far the most frequently used method to generate solid materials. Recently, several innovative crystallization tools have been reported, including high throughput screening (HTS) which has been fairly well integrated into pharmaceutical development. The emergence of this technology was largely fueled by the increasing drive to speed pharmaceutical development and yet capture solid form diversity as early and as thoroughly as possible. HTS is especially desirable, not only because it can be used to perform a few thousand crystallizations per week (almost completely automated), but also because it can achieve these results with only a few milligrams of API. HTS is a vital tool in process development as it is used to screen for conditions enabling purification of intermediates and API by crystallization and API form control.

HTS is generally conducted in a 96-well plate system in which a solution of the desired solid is dispensed using automated liquid handling systems. The amount of API reported per well for most HTS lies in the range of 0.25-10 mg. In this presentation, HTS was successfully conducted achieving crystallization with as little as 0.1 mg of API in a well. We will also report that even at a sample loading as low as 0.05 mg of API, a high quality PXRD pattern can be obtained. This illustrates the utility of HTS especially in the early stages of process development when API availability is limited.

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See more of this Session: Modeling for Process Optimization
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