Decision-support algorithms for biopharmaceutical portfolio & capacity management

Biopharmaceutical drug development is risky, lengthy, and expensive. Decisions in this delicate process are complicated by constraints on resources such as available capacity and uncertainties that include the risk of clinical failure. Hence, the impact of making sub-optimal decisions in this environment can be severe. Accordingly, this work explores the development of algorithms to support strategic drug development decisions and contains four results sections. Firstly, a decision-support framework based on multi-criteria decision making (MCDM) is presented for assessing options when acquiring biopharmaceutical manufacturing capacity. An example case illustrates the use of this framework where a biopharmaceutical company is faced with options for acquiring commercial manufacturing capacity. The development portfolio consists of three monoclonal antibody drugs at varying stages of clinical development with varying levels of demand. Capacity acquisition options include building in-house capacity, outsourcing, and partnering in addition to some hybrids of these. Deterministic and stochastic analyses showed that building manufacturing capacity ranked highest for the scenario considered when accounting for both financial and operational metrics. Secondly, the development of a stochastic combinatorial multi-objective optimisation framework is presented which confronts the problem of handling the multitude of decisions and trade-offs when designing portfolio management strategies, which results in extremely large decision spaces. The framework is considerate of strategic decisions that include the portfolio composition, the scheduling of critical development and manufacturing activities, and the involvement of third parties for these activities. The framework simulates development and manufacturing alongside the wider commercial environment. Machine learning and evolutionary computation techniques are also harnessed to characterise the conditional and probabilistic structure of superior decisions and evolve strategies to multi-objective optimality. A case study is constructed to derive insight from the framework where results demonstrate that a variety of options exist for formulating nondominated strategies in the objective space considered, giving the manufacturer a range of pursuable options. The most preferred means for development across the set of optimised strategies is to fully integrate development and commercial activities in-house, however, alternatives include partnering during early stages of portfolio development and then coordinating outsourced and in-house activities for remaining drugs. Popular scheduling strategies tend to develop two drugs in close succession while spacing out the remaining drug development activities into longer time frames. Thirdly, this framework is expanded to explore the impact of the size of biopharmaceutical drug development portfolio and cash flow constraints on algorithmically formulated strategies. Illustrative examples suggest that naively applying strategies optimal for a particular size of portfolio to a portfolio of another size is inappropriate. Also, the size of the portfolio appears to have a larger impact on strategy than the magnitude of cash flow constraint. Fourthly and finally, the economics of biopharmaceutical manufacture are explored with the aim of developing equations that can estimate the cost of manufacturing for both monoclonal antibodies and antibody fragments using mammalian cell culture and bacterial fermentation respectively. The correlations, derived using multiple linear regression, allow the cost of goods to be estimated given the following inputs: the required annual output, fermentation titre, whole process yield, and the probability of achieving a successful batch