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A framework for in-silico formulation design using multivariate latent variable regression methods.

机译:使用多变量潜变量回归方法进行计算机内配方设计的框架。

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摘要

A comprehensive Quality by Design development paradigm should consider the impact of raw materials and formulation on the final drug product. This work proposes a quantitative approach to simultaneously predict particle, powder, and compact mechanical properties of a pharmaceutical blend, based on that of the raw materials. A new, two-step, multivariate modeling method, referred to as the weighted scores PLS, was developed to address the challenge of predicting the properties of a powder blend while enabling process understanding. The model validation exercise is shown along with selected practical applications. It is shown how the proposed in-silico model exhibits sufficient predictive power to be an important tool in the pharmaceutical development decision making process while requiring minimal experimentation and material usage.
机译:全面的“按质量设计”开发范例应考虑原材料和配方对最终药品的影响。这项工作提出了一种基于原料的定量方法,可同时预测药物混合物的颗粒,粉末和致密机械性能。开发了一种新的两步多元建模方法,称为加权分数PLS,以解决在预测粉末共混物性能的同时又能使工艺理解的挑战。显示了模型验证练习以及所选的实际应用。显示了所提出的计算机模拟模型如何展现出足够的预测能力,成为药物开发决策过程中的重要工具,同时需要最少的实验和材料使用。

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