Virtual Formulation Laboratory for prediction and optimisation of manufacturability of advanced solids based formulations

固体剂型是许多制造业的支柱。在药物治疗领域，片剂、胶囊、干粉吸入器和用于再悬浮的粉末占据了英国该行业56亿英镑销售额的绝大部分。食品（销售额670亿英镑）是英国制造业中最大的单一产业，2014年的总销售额为3650亿英镑（国家统计局）。在所有这些制造过程和最终使用中，粉末的物理性能至少与化学性能同样重要。稳定性、重量和含量均匀性、制造难度和可变性能是由配方过程中的决策决定的，制造问题无处不在；兰德报告（由E.W. Merrow撰写，1981年）研究了粉末工艺，发现由于粉末性能和工艺设计之间的不兼容性，达到全部生产力平均需要2年的时间，开发成本是预计的210%。在这期间，植物工程技术得到了发展，但配方决策的合理化从未得到过粗略的实证研究。该项目建议开发一个虚拟配方实验室（VFL），这是一个软件工具，用于预测和优化先进固体配方的可制造性和稳定性。该团队在粉末流动，混合和压实方面建立了专业知识，这将首次将配方变量与可制造性预测联系起来。该项目的总体目标是(a)发展理解表面、颗粒结构和整体行为的科学基础，以解决加工和储存过程中的物理、化学和机械稳定性问题；(b)将这些纳入一个软件工具（VFL），该工具适用于各种材料类型。颗粒结构和混合系统，使配方师能够在虚拟空间中测试配方变化的效果，并检查潜在的问题，这些问题涵盖了生产工厂中遇到的大多数制造困难。VFL的VISION将在项目完成后的五年内广泛应用于食品、药品和精细化学品中每一种新配方粉末产品的开发过程。VFL将考虑四个过程：粉末流动、混合、压实和储存；并将预测四个可制造性问题：流动性差/泛水、偏析/非均质性、粉末结块和压实物的强度/断裂。这些问题占固体颗粒材料加工中的大多数实际问题。(a)填补配方科学的空白，将分子与可制造性联系起来，这将通过实验表征和数值模拟来实现，以及(b)建立处理新材料的方法，以便虚拟实验室可以在没有广泛的实验表征或数值模拟的情况下对新材料的配方进行预测。这将通过基于上述调查的科学成果发展功能关系来实现，同时确定这些关系的局限性和不确定性。

Solid dose forms are the backbone of many manufacturing industries. In pharmaceutical therapeutics, tablets, capsules, dry powder inhalers and powders for re-suspension cover the vast majority of the £5.6Bn sales by this industry in the UK. Food (sales £67Bn) is the single largest industry of the UK manufacturing sector which totalled £365Bn sales in 2014 (Office of National Statistics). In all these manufacturing processes and in final use, the physical behaviour of the powder is at least as important as the chemistry. Stability, weight and content uniformity, manufacturing difficulties and variable performance are determined by decisions made during the formulation process Manufacturing problems are ubiquitous; the Rand report (by E.W. Merrow, 1981) examined powder processes and found on average 2 year over-runs to get to full productivity, and development costs 210% of estimates, due to incompatibility between powder behaviour and process design. In the intervening years, plant engineering techniques have developed, but the rationalisation of formulation decisions has never received more than cursory, empirical study. This project proposes to develop a Virtual Formulation Laboratory (VFL), a software tool for prediction and optimisation of manufacturability and stability of advanced solids-based formulations. The team has established expertise in powder flow, mixing and compaction which will be brought together for the first time to link formulation variables with manufacturability predictions. The OVERALL AIMS of the project are (a) to develop the science base for understanding of surfaces, particulate structures and bulk behaviour to address physical, chemical and mechanical stability during processing and storage and (b) to incorporate these into a software tool (VFL) which accounts for a wide range of material types, particle structures and blend systems to enable the formulator to test the effects of formulation changes in virtual space and check for potential problems covering the majority of manufacturing difficulties experienced in production plants. The VISION for VFL is to be employed widely in the development process of every new formulated powder product in food, pharmaceuticals and fine chemicals within five years of the completion of this project. VFL will consider four processes: powder flow, mixing, compaction and storage; and will predict four manufacturability problems: poor flow/flooding, segregation/heterogeneity, powder caking and strength/breakage of compacts These account for the majority of practical problems in the processing of solid particulate materials The OVERALL OBJECTIVES of the project are: (a) to fill the gaps in formulation science to link molecule to manufacturability, which will be achieved through experimental characterisation and numerical modelling, and (b) establish methodologies to deal with new materials, so that the virtual lab could make predictions for formulations with new materials without extensive experimental characterisation or numerical modelling. This will be achieved through developing functional relationships based on the scientific outcomes of the above investigations, while identifying the limits and uncertainties of these relationships.