A COMPUTATIONAL FRAMEWORK FOR MODERN HERBAL DRUG DEVELOPMENT: "HERBAL DRUG MANUFACTURING: LAB - SMALL - LARGE SCALE INTEGRATED WITH ARTIFICIAL INTELLIGENCE"

Herbal drug manufacturing is receiving increasing global interest because of the growing preference for natural, safe, and sustainable therapeutic goods. However, scaling up formulations from laboratory level to pilot and industrial production remains a significant challenge. Traditional scale-up methods mainly depend on trial-and-error approaches, which often lead to inconsistencies between batches, degradation or loss of active phytochemicals, lower extraction efficiency, excessive use of solvents and energy, and higher production costs. In addition, the lack of real-time monitoring and effective process control can negatively affect product quality and regulatory compliance. To overcome these challenges, the adoption of Artificial Intelligence (AI) along with advanced process analytical technologies has emerged as an effective solution. AI-driven systems are capable of predicting optimal extraction and processing conditions, reducing the need for repeated experiments, enabling real-time tracking of critical parameters, maintaining batch uniformity, improving yield, and optimizing resource usage. Furthermore, these technologies strengthen quality assurance and documentation processes. The integration of intelligent, data-driven approaches in herbal drug manufacturing not only increases efficiency but also supports sustainability and enhances global competitiveness. In the future, AI-powered smart manufacturing systems are expected to transform the herbal pharmaceutical industry by speeding up commercialization while ensuring safety, effectiveness, and regulatory compliance (Xiong, Hs. et al., 2023).