Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Pharmaceutical Company's Choices of Indication for the First Clinical Projects in Oncological Drug Development in the United States

We analyzed factors shaping the choice of the lead indication (i.e., cancer type) in the first clinical development projects of new oncological drugs in the United States (US), and how the type of pharmaceutical company is related to this choice. We selected 576 new clinical development projects in the US since 2000 for analysis. These projects were characterized according to three potential perspectives detected by multiple correspondence analysis: the morbidity of the cancer type which corresponds to market size of each cancer type, the company's previous experience with the cancer type, and the company's attitude to development risks. Mega firms tend to choose cancer types with higher morbidity (and large-market), previously experienced cancer types, while diverse small firms choose both major and rare cancers and both high- and low-risk projects, indicating that different sizes of firms utilize different development entry patterns. Common tendencies concerning the choice of lead indication were found across all companies. Cancer types the company had developed and launched in the past were more likely to be chosen; cancer types with high five-year survival rates and those with high competition were less likely to be chosen. The study showed that pharmaceutical companies seem to enter clinical development from cancer types where they can demonstrate their strengths and advantages through experience, depending on each cancer type's different market sizes and development difficulties. The results could provide clues for considering what support measures and incentives are appropriate to balance the efficiency of industrial development and the fulfillment of society's unmet medical needs.

Trends, challenges, and success factors in pharmaceutical portfolio management: Cognitive biases in decision-making and their mitigating measures

Effective portfolio management is crucial for innovation and sustaining revenue in pharmaceutical companies. This article holistically reviews trends, challenges, and approaches to pharmaceutical portfolio management and focuses, in particular, on cognitive biases in portfolio decision-making. Portfolio managers strongly rely on external innovation and face increasing competitive pressure and portfolio complexity. The ability to address biases and make robust decisions remains a challenge. Portfolio management practitioners most commonly face confirmation bias, champion bias, or misaligned incentives, which they seek to mitigate through expert input, team diversity, and rewarding truth-seeking. Ultimately, highest-quality portfolio management decision-making could be enabled by three factors: high-quality data, structured review processes, and comprehensive mitigating measures against biases in decision-making.

Stacking Machine Learning Model for the Assessment of R&D Product’s Readiness and Method for Its Cost Estimation

The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products’ commercialization processes. However, in some cases, we cannot consider any single technique to be the best one as each of them has advantages and disadvantages. In such conditions, all efforts should be made to use a combination of the estimation techniques to arrive at a better cost and quality estimate. The effectiveness of the valuation of R&D products is of particular importance in today’s economy and due to the need to analyze large data sets prepared for transfer from universities to the business environment. This paper presents the model, two methods, and general information technology for R&D products’ readiness level assessment and R&D products’ cost estimation. The article presents the complex method for determining the cost of R&D products, which will allow: increasing the efficiency of the transfer, commercialization, and market launch of R&D products, and promoting the interaction of all components of the national innovation infrastructure, innovations, etc. The need to consider many different indicators when evaluating R&D products has determined the need to use machine learning algorithms. We have designed a new machine learning-based model for the readiness assessment of R&D products, which is based on the principle of “crowd wisdom” and uses a stacking strategy to integrate machine learning methods. It is experimentally established that the new stacking model based on machine learning algorithms that use random forest as a meta-algorithm provides a minimum of a 1.03 times higher RMSE compared to other ensemble strategies.