Developability profile framework for lead candidate selection in topical dermatology.

The development of molecules for topical dermatology has primarily relied on drug repurposing or on combination therapies, leading to an average of only one New Chemical Entity (NCE) approved per year by the FDA. Topical products offer benefits to patients by enabling localized treatment, while minimizing systemic exposure and the likelihood of adverse events. New therapies are further justified by the burden skin diseases cause on patients' quality of life. Notwithstanding the opportunities, the selection of a topical NCE presents challenges, primarily derived from a target product profile uncommon to oral drugs. Beyond a more stringent range of physicochemical properties, the molecule must display adequate solubility and chemical stability in topical-relevant excipients; must effectively cross the stratum corneum, considerably less permeable than the intestinal epithelium, and elicit a local therapeutic response; and must enable a formulation with robust physical stability. A novel framework intended to de-risk NCE selection is presented and based on four calculated physicochemical properties: molecular weight, clogP, topological polar surface area, and aromatic ring count. The use of topical-relevant solvents to assess the molecule's solubility profile, and a 2-day accelerated chemical stability methodology, are also described as critical steps in early dermal development.

High-throughput blend segregation evaluation using automated powder dispensing technology.

Due to the complexity in the interactions of variables and mechanisms leading to blend segregation, quantifying the segregation propensity of an Active Pharmaceutical Ingredient (API) has been challenging. A high-throughput segregation risk prediction workflow for early drug product development has been developed based on the dispensing mechanism of automated powder dispensing technology. The workflow utilized liquid handling robots and high-performance liquid chromatography (HPLC) with a well-plate autosampler for sample preparation and analysis. Blends containing three different APIs of varying concentrations and particle sizes of different constituents were evaluated through this automated workflow. The workflow enabled segregation evaluation of different API blends in very small quantities (~7g) compared to other common segregation testers that consume hundreds of grams. Segregation patterns obtained were well explained with vibration induced percolation-based segregation phenomena. Segregation risk was translated quantitatively using relative standard deviation (RSD) calculations, and the results matched well with large-scale segregation studies. The applied approach increased the throughput, introduced a simple and clean walk-up method with minimized equipment space and API exposures to conduct segregation studies. Results obtained can provide insights about optimizing particle size distributions, as well as selecting appropriate formulation constituents and secondary processing steps in early drug product development when the amount of available API is very limited.

Underpinning mechanistic understanding of the segregation phenomena of pharmaceutical blends using a near-infrared (NIR) spectrometer embedded segregation tester.

The segregation of an active pharmaceutical ingredient (API) within a powder blend is one of the major manufacturing obstacles in achieving content uniformity. Segregation can be due to differences in physicochemical properties of formulation components and/or perturbations experienced during secondary processing steps, such as granulation, fluidization, die-filling and compression. A near-infrared (NIR) spectrometer embedded segregation tester, which could mimic the external stimulations (vibration and fluidization) experienced by a blend in a manufacturing facility, was used to evaluate and predict blend segregation. Two different GlaxoSmithKline (GSK) product blends with variations in the API particle size and concentration were tested. Drug content was further measured at different locations along the powder bed by NIR to sketch the segregation profile and calculate the overall segregation intensity of each blend. The study indicated that the segregation potential was dependent on the particle sizes of API and excipients, as well as the type of stimulus applied (vibration vs fluidization). Drug concentration profiles obtained from this mode of analysis decoded the underlying segregation mechanisms (sieving, trajectory and air elutriation) easily. The employed NIR-based segregation tester proved to be a useful small-scale predictive tool to evaluate and rank the segregation risk of the studied pharmaceutical blends.