Computational pharmaceutics - A new paradigm of drug delivery

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Poor aqueous solubility of drugs is still a foremost challenge in pharmaceutical product development. The use of lipids in designing formulations provides an opportunity to enhance the aqueous solubility and consequently bioavailability of drugs. Pre-dissolution of drugs in lipids, surfactants, or mixtures of lipid excipients and surfactants eliminate the dissolution/dissolving step, which is likely to be the rate-limiting factor for oral absorption of poorly water-soluble drugs. In this review, we exhaustively summarize the lipids excipients in relation to their classification, absorption mechanisms, and lipid-based product development. Methodologies utilized for the preparation of solid and semi-solid lipid formulations, applications, phase behaviour, and regulatory perspective of lipid excipients are discussed.

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Fundamental Aspects of Lipid-Based Excipients in Lipid-Based Product Development

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Graphical Abstract Impact of various data learning approaches on NIR spectral data

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Haoyue Jia

Papers

Integrated in silico formulation design of self-emulsifying drug delivery systems