Foreword

Due to the significant advantages that they offer, in silico technologies, e.g., computer-aided (CA) simulation technology, machine learning, and artificial intelligence, have been attracting great attention in the pharmaceutical industry. It goes without saying that drug discovery and development require lengthy and high-risk processes (taking 10–15 years, or more), and billions of U.S. dollars, until a new drug is approved for clinical use. During the stages of pharmaceutical development, ranging from preformulation to commercial manufacturing, it is necessary to optimize the dosage form, formulation, and manufacturing process. Because there are numerous formulation variables and process parameters that may affect the quality and characteristics of pharmaceuticals, these development stages involve tremendous costs in terms of budget and labor. In silico technologies have great potential to reduce such costs. Furthermore, after introducing the concept of “Quality by Design (QbD)” into the pharmaceutical industry, the formulation design needs to be performed based on a systematic and scientific approach. In recent years, in silico technologies have begun to be regarded as being a powerful tool to implement the QbD approach. It is against this background that the current topic features the application of in silico technologies to pharmaceutical research.

The first article is a review article entitled “Prediction of Critical Quality Attributes Based on the Numerical Simulation of Stress and Strain Distributions in Pharmaceutical Tablets” by Prof. Takayama et al. This article presents a CA simulation study to understand the stresses and strains generated on the surface and inside of pharmaceutical tablets during the tableting process. Because it is difficult to obtain these parameters experimentally, the author integrated a numerical simulation, the so-called finite element method, into the research project. On completion of the study, CA simulations were proven to be a promising tool to investigate the mechanical stress on the top and lateral surfaces of tablets after the application of external forces.

The second article is a review article entitled “Application of in Silico Technologies for Drug Target Discovery and Pharmacokinetic Analysis” by Prof. Iwata. Computational methods such as molecular dynamics simulation and machine learning tools are considered to be powerful in drug discovery. The earlier part of the review article describes the basic concept of the “in silico drug discovery.” The latter part of the article introduces a novel challenge, to predict human pharmacokinetic parameters from nonclinical data using the in silico method. The author emphasizes that these in silico technologies will enable us to reduce research and development costs, improve the probability of success, and increase process efficiency.

The third article is a regular article entitled “A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning” by Dr. Hayashi et al. The focus of this study is the precise prediction of tablet characteristics from the fundamental information of a tablet’s materials using machine learning. This study employed random forest as a machine learning algorithm. On completion of the research, the tensile strength and disintegrating time were precisely predicted from the physicochemical properties and molecular descriptors of the tablet’s materials. The authors concluded that the data-driven approach is indeed useful for designing desirable tablet formulations.

The fourth article is a regular article entitled “Optimization and Advantages of Molded Tablets Using Trehalose as a Binder” by Prof. Yonemochi and colleagues. It describes a formulation optimization study of a novel orally disintegrating tablet, a so-called “molded tablet.” Herein, trehalose was applied as a new binder of molded tablets. Design of experiment and response surface methodology were employed for optimizing tablet formulation. This study contains good recommendations for manufacturing novel orally disintegrating tablets.

I believe that this Current Topics offers valuable information about in silico technologies, which will significantly contribute to pharmaceutical development. In conclusion, I would like to express my gratitude to all the authors for their significant contributions, enabling the completion of this Current Topics.