Quality by Computational Design: Harnessing AI to Advance Rational Drug Development

The pharmaceutical Quality by Design (QbD) principle aims to reduce risk and improve efficiency across drug development lifecycle. However, QbD was originally established in an era preceding the widespread adoption of artificial intelligence (AI) and did not fully capture the potential of computational pharmaceutics. This gap is particularly pronounced in complex drug product development, where conventional QbD relies on empirical knowledge and labor-intensive experimentation. As a result, it struggles to accommodate multi-modal and multi-scale variables, and lacks sufficient flexibility, dynamic optimization capabilities, and the ability to perform clinically oriented inverse design. In recent years, advances in computational pharmaceutics have provided a new methodological foundation for drug development. In this context, we propose a novel paradigm, termed Quality by Computational Design (QbCD), which integrates computational pharmaceutics within the QbD framework to achieve mechanism-based and clinically guided formulation design. We first define the concept of QbCD, outline its essential components, implementation steps, and methodological strengths, and discuss relevant regulatory considerations. Building on this, we propose a practical QbCD implementation guideline to strengthen model credibility and ensure regulatory compliance. Subsequently, to establish the methodological foundation and demonstrate practical feasibility, we present the core techniques of QbCD, including AI, physical modeling, and in vivo modeling, and examine their applications across various stages of drug development. To further illustrate the practicality of QbCD, two representative cases are presented: a QbCD-enabled virtual development workflow for amorphous solid dispersions and a real-world implementation of QbCD in designing long-acting in situ gel injectables. Finally, we discuss future perspectives for QbCD, focusing on bridging the data gap, advancing methodological innovations, enhancing model credibility and regulatory compliance, and fostering a supportive scientific culture and ecosystem in computational pharmaceutics. These efforts aim to promote a more intelligent, efficient, and clinically aligned paradigm for rational drug development.