Predictive Modeling for Portfolio Risk Assessment in Multi-Therapeutic Pharmaceutical Enterprises

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.