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This commentary presents contributions and accomplishments of Professor Saranjit Singh, National Institute of Pharmaceutical Education and Research (NIPER), SAS Nagar, India, to pharmaceutical research and education. Prof. Singh completed his successful tenure in October 2021. Over his 40+ years of illustrious academic career, he trained 147 Masters and 15 PhD students in the fields of drug stability testing, degradation chemistry, impurity and metabolite characterization, and advanced analytical technologies. He has published ∼250 research articles, reviews, editorials, patent, book, and book chapters, and received numerous awards, including the Professor M.L. Khorana Memorial Lecture Award from the Indian Pharmaceutical Association (IPA) and the Outstanding Analyst and Eminent Analyst awards from the Indian Drug Manufacturers' Association (IDMA). This commentary highlights Prof. Singh's inspiring personal and renowned professional journey, including early life, education, career, accomplishments, as well as his services to academia, industry, and regulatory. By sharing the contributions and accomplishments of Prof. Singh, we strongly believe that his story will inspire the next generation of scientists to continue his legacy to advance the field.

Abstract An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al.. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset (http://www.models.life.ku.dk/Tablets) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract