A Systematic Method Development Strategy for Quantitative Color Measurement in Drug Substances, Starting Materials, and Synthetic Intermediates

Abstract

The color of active pharmaceutical ingredients (APIs) can be a critical quality attribute for pharmaceutical products. Color variation can be indicative of contaminants, chemical impurities, solid-state form impurities, or degradation products. The color of the pharmaceutical materials is typically specified and determined using visual appearance testing. To avoid the inherent subjectivity of visual appearance testing, to understand the link between the color and the process parameters, and to facilitate quality agreements between customers and vendors of chemical starting materials, quantitative color measurements utilizing visible spectroscopic methods are increasingly needed. In this work, a systematic method development strategy (MDS) is described to assist with the development of robust and rugged quantitative color measurement methods based on a science-and risk-based approach aligned with quality-bydesign principles. The MDS is illustrated using typical scenarios encountered in method development through several examples including a method developed for 2,3-dimethyl-2H-indazol-6-amine, a starting material in the commercial synthesis of pazopanib hydrochloride API.Flowcharts and examples are presented to illuminate key decision points in the MDS process including selection of (1) solution-state versus solid-state spectroscopic method, (2) optimal color space for reporting color measurement results, and (3) appropriate method risk assessment and control to ensure successful method implementation.General guidance is also provided to facilitate the discussion for setting quantitative color specifications.

Development of Quality-By-Design Analytical Methods

“…In light of the variety of analytical methods that can potentially benefit from QbD approaches, a structured approach to analytical method development that transcends the technique used is necessary, such as the MDS approach. [8][9][10][11][12] The structured steps utilized in the MDS approach are shown in Figure 1 and begin with the definition of a goal for the method and collection of background information, systematic scouting and evaluation of alternative methods, selection of a promising method that meets the goal, assessment of the method for risks using structured tools, and finally development of a control strategy to ensure method performance. The MDS approach emphasizes the use of scientific understanding and QbD principles during each step in method development, rather that testing quality into an already-developed method.…”

“…7 The QbD concept can be extended to analytical methods. [8][9][10][11][12][13][14][15][16][17][18][19] This paper summarizes the current state of QbD as applied to the development of analytical methods small-molecule pharmaceutical process development, including synthesis of the active pharmaceutical ingredient (API) and formulation, and biopharmaceutical process development. In this section, we introduce the principles of QbD and briefly review the overall status of QbD in the pharmaceutical industry to illustrate the position that analytical QbD occupies in this paradigm.…”

Use of solid dispersions to increase stability of dithranol in topical formulations

“…The a* parameter represents changes along a red-green axis with changes from +60 for a red surface to -60 to a green surface. The b* parameter changes from +60 for yellow surface to -60 for a blue surface (Clarys et al, 2000;Zhou et al, 2011). The surface measured is 8 mm in diameter.…”

Development of fast dispersing tablets of nebivolol: experimental and computational approaches to study formulation characteristics

“…Three super disintegrants viz., Ac-Di-Sol (Cross-linked carboxymethyl cellulose), Primojel (sodium salt of carboxymethyl ether of starch) and Polyplasdone XL (Cross-linked poly vinyl pyrrolidone) were tested individually and in combination. The experiment was executed using simplex lattice design (SLD) in order to simplify the formulation procedure and to optimize the formulation with limited number of trials (Zhou et al,2011). SLD is useful when the trial -error approach may not be possible for the formulation design and the relationship between influential factors and output response in the work is complex.…”