Pharmaceutical portfolio optimization under cost uncertainty via chance constrained-type method

Farid, Mahboubeh; Chaudhry, Abraar; Ytterstad, Magnus; Wiklund, Stig Johan

doi:10.1186/s13362-021-00099-3

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…We envisage a couple of ways in which the current approach could be extended to a more complex, and arguably more realistic, situation. One example might be to represent costs and revenues with uncertainty, that is, to model them as stochastic distributions rather than representing them with expected values (see e.g., Farid et al 7 for optimisation methods of pharmaceutical portfolios under uncertainty). Extending the inclusion of uncertainty into the modelling, the risk‐adjustment of cost and revenues could be represented by bimodal distributions.…”

Section: Discussionmentioning

confidence: 99%

“…Much attention has been paid to the issue of how to select an optimal subset of available projects to fit within the available budget. [2][3][4][5][6][7][8][9] But drug development is not only costly, it is also highly uncertain. Given a success rate of only 10%-15% from the start of the clinical phase, 10 the most likely outcome of any drug project is a failure in one of the clinical phases.…”

Section: Introductionmentioning

confidence: 99%

“…Since drug development is exceptionally costly, with the cost per each successfully launched drug being hundreds of millions of dollars, 1 it is often the case that the budget allocated to R&D may not be sufficient to fund all the available projects for development. Much attention has been paid to the issue of how to select an optimal subset of available projects to fit within the available budget 2–9 . But drug development is not only costly, it is also highly uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the topic of portfolio selection has been thoroughly investigated; Blau et al 4 suggest a genetic algorithm to search for the optimal selection of projects from the current development portfolio in the presence of operational risks, project interdependencies, and resource constraints. Farid et al 7 use a chance‐constrained method, accounting for cost uncertainties. In Reference 11, a multi‐objective method is used while accounting for uncertainty in both cost and revenue.…”

Section: Introductionmentioning

confidence: 99%

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Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Wiklund

Önnheim

Ytterstad

2023

Pharmaceutical Statistics

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Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this paper, we argue the need for a forward‐looking approach to portfolio decision‐making. We develop a quantitative model that allows the portfolio management to evaluate the need for future inflow of new projects to achieve revenue at desired levels, often aspiring to a certain annual revenue growth. Optimisation methods are developed for the presented model, allowing an optimal choice of number, timing and type of projects to be added to the portfolio. The proposed methodology allows for a proactive approach to portfolio management, prioritisation, and optimisation. It provides a quantitatively based support for strategic decisions regarding the efforts needed to secure the future development pipeline and revenue stream of the company.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Wiklund

Önnheim

Ytterstad

2023

Pharmaceutical Statistics

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“…Recently, Farid et al [13] presented an approach for solving pharmaceutical portfolio optimization, where the coefficients of decision variables in constraints make up a finite set of random variables with unknown distributions. They employed chance constraint programming to model a portfolio optimization problem and then found the optimal solution for the portfolio by using binary control variables z k , k = 1, 2, .., N and "big M" coefficients.…”

Section: Goal-programming With Data Uncertaintymentioning

confidence: 99%

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Farid¹,

Hallman²,

Palmblad³

et al. 2021

Mathematics

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This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

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Going beyond probability of success: Opportunities for statisticians to influence quantitative decision‐making at the portfolio level

Wiklund,

Thorn,

Götte

et al. 2024

Pharmaceutical Statistics

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The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves managing risk, and statisticians are ideally positioned to champion not only the derivation of metrics for individual projects, but also advocate decision‐making at a broader portfolio level. This article aims to examine the existing different portfolio decision‐making approaches and to suggest opportunities for statisticians to add value in terms of introducing probabilistic thinking, quantitative decision‐making, and increasingly advanced methodologies.

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Pharmaceutical portfolio optimization under cost uncertainty via chance constrained-type method

Cited by 7 publications

References 23 publications

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Going beyond probability of success: Opportunities for statisticians to influence quantitative decision‐making at the portfolio level

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