EPSRC CMAC Future Manufacturing Research Hub, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK; Strathclyde Institute of Pharmacy & Biomedical Sciences (SIPBS), University of Strathclyde, Glasgow G4 0RE, UK.
GlaxoSmithKline R&D, Park Road, Ware, Herts SG12 0DP, UK.
Int J Pharm. 2022 Oct 15;626:122116. doi: 10.1016/j.ijpharm.2022.122116. Epub 2022 Aug 18.
Recent years have seen the advent of Quality-by-Design (QbD) as a philosophy to ensure the quality, safety, and efficiency of pharmaceutical production. The key pharmaceutical processing methodology of Direct Compression to produce tablets is also the focus of some research. The traditional Design-of-Experiments and purely experimental approach to achieve such quality and process development goals can have significant time and resource requirements. The present work evaluates potential for using combined modelling and experimental approach, which may reduce this burden by predicting the properties of multicomponent tablets from pure component compression and compaction model parameters. Additionally, it evaluates the use of extrapolation from binary tablet data to determine theoretical pure component model parameters for materials that cannot be compacted in the pure form. It was found that extrapolation using binary tablet data - where one known component can be compacted in pure form and the other is a challenging material which cannot be - is possible. Various mixing rules have been evaluated to assess which are suitable for multicomponent tablet property prediction, and in the present work linear averaging using pre-compression volume fractions has been found to be the most suitable for compression model parameters, while for compaction it has been found that averaging using a power law equation form produced the best agreement with experimental data. Different approaches for estimating component volume fractions have also been evaluated, and using estimations based on theoretical relative rates of compression of the pure components has been found to perform slightly better than using constant volume fractions (that assume a fully compressed mixture). The approach presented in this work (extrapolation of, where necessary, binary tablet data combined with mixing rules using volume fractions) provides a framework and path for predictions for multicomponent tablets without the need for any additional fitting based on the multicomponent formulation composition. It allows the knowledge space of the tablet to be rapidly evaluated, and key regions of interest to be identified for follow-up, targeted experiments that that could lead to an establishment of a design and control space and forgo a laborious initial Design-of-Experiments.
近年来，质量源于设计（QbD）理念的出现确保了药物生产的质量、安全性和效率。直接压片作为一种关键的制药加工方法，也成为了一些研究的焦点。传统的设计实验和纯实验方法来实现这种质量和工艺开发目标可能需要大量的时间和资源。本工作评估了使用组合建模和实验方法的潜力，这种方法可以通过从纯成分压缩和压缩模型参数预测多成分片剂的性质来减轻这种负担。此外，还评估了从二元片剂数据外推来确定无法以纯形式压缩的材料的理论纯成分模型参数的可能性。结果发现，使用二元片剂数据外推是可能的，其中一种已知的成分可以以纯形式压缩，而另一种是难以压缩的成分。已经评估了各种混合规则，以评估哪种规则适合多成分片剂性质的预测，在本工作中，发现使用预压缩体积分数的线性平均是最适合压缩模型参数的，而对于压缩，发现使用幂律方程形式的平均与实验数据的一致性最好。还评估了不同的方法来估计成分体积分数，发现使用基于纯成分压缩理论相对速率的估计方法略优于使用常数体积分数（假设混合物完全压缩）的方法。本工作中提出的方法（必要时外推二元片剂数据，结合使用体积分数的混合规则）提供了一种无需基于多成分配方组成进行任何额外拟合的多成分片剂预测框架和途径。它允许快速评估片剂的知识空间，并确定后续目标实验的关键关注区域，这些实验可能导致设计和控制空间的建立，并避免繁琐的初始设计实验。
