Reaction Kinetics and Conversion Efficiency Analysis of Chemical Precursors in Active Pharmaceutical Ingredient Synthesis: A Data-Driven Approach Based on Organic Compounds (HS Code 29)

Abstract

The pharmaceutical industry increasingly relies on efficient chemical precursor conversion and optimized reaction kinetics to improve the synthesis of active pharmaceutical ingredients (APIs). Organic compounds classified under Harmonized System (HS) Code 29 are widely used as chemical intermediates because of their functional versatility and high reactivity. This study aimed to analyze the reaction kinetics and conversion efficiency of selected chemical precursors used in API synthesis in Indonesia using a data-driven approach. Secondary data were obtained from Statistics Indonesia (BPS), UN Comtrade, Indonesian Ministry of Industry reports, and peer-reviewed scientific literature published between 2018 and 2025. The study focused on alcohols, ketones, esters, and amines imported and processed within pharmaceutical manufacturing industries in West Java, Banten, and East Java. The analysis combined pseudo-first-order kinetic modeling, Arrhenius-based reaction evaluation, and conversion efficiency calculations based on process yield indicators. The results showed that precursor purity, reaction temperature, catalyst concentration, and solvent polarity significantly influenced reaction rates and conversion efficiency. Ester-based intermediates achieved conversion efficiencies exceeding 85%, particularly in antibiotic and analgesic synthesis. Furthermore, optimizing reaction temperatures between 60 and 90°C increased reaction rate constants while reducing undesired side reactions, resulting in improved process efficiency. These findings demonstrate that integrating industrial trade data with reaction kinetics analysis provides a practical framework for evaluating precursor performance and supports sustainable pharmaceutical manufacturing optimization in developing countries. Future research should incorporate real-time reactor monitoring and machine learning-assisted kinetic prediction to improve process optimization and industrial-scale pharmaceutical production.