Authors : Ezichi Adanna Anokwuru; Joy Onma Enyejo

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/2mamxww7

Scribd : https://tinyurl.com/3her3zd4

DOI : https://doi.org/10.38124/ijisrt/25nov1475

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Pharmaceutical enterprises operating across multiple therapeutic areas face increasingly complex risk environments driven by evolving regulatory policies, global market volatility, and shifting R&D productivity dynamics. This review presents a comprehensive examination of predictive modeling frameworks designed to enhance portfolio risk assessment for multi-therapeutic organizations, with emphasis on oncology, cardiovascular, and vaccine development pipelines. The paper evaluates the application of Python-based Monte Carlo simulations for probabilistic forecasting of clinical success rates, cost overruns, regulatory delays, and market adoption uncertainties. Additionally, Bayesian network models are analyzed as tools for representing causal dependencies among scientific, operational, and macroeconomic variables that influence therapeutic portfolio performance. The integration of these quantitative methods provides a robust foundation for stress-testing strategic investment decisions, optimizing resource allocation, and supporting evidence-based governance under varying policy scenarios. By synthesizing current methodologies, case applications, and computational best practices, this review highlights emerging opportunities for data-driven risk intelligence in pharmaceutical portfolio management. The findings underscore the need for adaptive, transparent, and scalable analytical systems capable of guiding long-term enterprise decision-making amidst global health and economic disruptions.

Keywords : Portfolio Risk Assessment; Monte Carlo Simulation; Bayesian Networks; Multi-Therapeutic Pharmaceuticals; Predictive Modeling.

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