NERDG 2026
Abstracts for Round Table (RT) Session 2: Predictive Platforms - DS and DP
Presentation 1
Model-based predictive tools to assess critical quality attributes of drug products
Dr. Shubhajit Paul, Boehringer Ingelheim
Abstract:
There are several opportunities and challenges in expediting drug product development timeline due to resource availability, long lead time to market, regulatory protocols, competitor drugs, etc. Therefore, establishing predictive tools is highly advantageous to offset experimental burden and align with only few experiments to meet desired quality attributes of drug products. This talk will focus on specific predictive modeling tools to demonstrate the workflow of advanced predictive platforms for assessing critical quality attributes of drug products at various stages. The examples will include deploying material-sparing approaches to optimize formulation characteristics, roller compaction process space, and minimizing the sticking and capping/lamination tendencies in a formulation. Understanding the workflow of such predictive tools is expected to facilitate the integration of these concepts in drug product workpackages expediting the development timelines.
Abstracts for Round Table (RT) Session 2: Predictive Platforms - DS and DP
Presentation 1
Model-based predictive tools to assess critical quality attributes of drug products
Dr. Shubhajit Paul, Boehringer Ingelheim
Abstract:
There are several opportunities and challenges in expediting drug product development timeline due to resource availability, long lead time to market, regulatory protocols, competitor drugs, etc. Therefore, establishing predictive tools is highly advantageous to offset experimental burden and align with only few experiments to meet desired quality attributes of drug products. This talk will focus on specific predictive modeling tools to demonstrate the workflow of advanced predictive platforms for assessing critical quality attributes of drug products at various stages. The examples will include deploying material-sparing approaches to optimize formulation characteristics, roller compaction process space, and minimizing the sticking and capping/lamination tendencies in a formulation. Understanding the workflow of such predictive tools is expected to facilitate the integration of these concepts in drug product workpackages expediting the development timelines.
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Bio:
Dr. Shubhajit Paul is currently a Senior Research Fellow in the Material and Analytical Sciences Department at Boehringer Ingelheim, USA with 12+ years of experience in solid dosage form development, material science, and predictive modeling. He graduated from National University of Singapore in 2013 with the major in Biopharmaceutics and worked in Prof. Calvin Sun’s lab at University of Minnesota as postdoctoral researcher on various industry-funded projects in the field of material science driven solid dosage design, optimization and risk assessments. In his current role at Boehringer over past 7 years, he leads the material characterization and process modeling group to expedite drug product development through optimal formulation and process design. Shubhajit has been recipient of President’s award, BI’s highest recognition for developing a tailored workflow of formulation and process optimization using few grams of API. He has authored/coauthored more than 50 publications including research articles, reviews and book chapters.
Dr. Shubhajit Paul is currently a Senior Research Fellow in the Material and Analytical Sciences Department at Boehringer Ingelheim, USA with 12+ years of experience in solid dosage form development, material science, and predictive modeling. He graduated from National University of Singapore in 2013 with the major in Biopharmaceutics and worked in Prof. Calvin Sun’s lab at University of Minnesota as postdoctoral researcher on various industry-funded projects in the field of material science driven solid dosage design, optimization and risk assessments. In his current role at Boehringer over past 7 years, he leads the material characterization and process modeling group to expedite drug product development through optimal formulation and process design. Shubhajit has been recipient of President’s award, BI’s highest recognition for developing a tailored workflow of formulation and process optimization using few grams of API. He has authored/coauthored more than 50 publications including research articles, reviews and book chapters.
Presentation 2
A Predictive Modeling Platform for Accelerating Drug Substance, Drug Product, Formulation Development and Delivery Timelines
Dr. Shiva Sekharan, Schrodinger, Inc.
Abstract:
Early assessment of degradation, reactivity, catalysis, polymorphism and solubility of active pharmaceutical ingredients (API) is critical for small molecule drug substance and drug product development processes. We have developed an automated computational platform leveraging physics-based methods, chemistry-informed AI and ML models to efficiently 1) predict bond dissociation energies and decomposition products to elucidate reaction mechanisms, 2) screen crystal polymorphs to derisk selection of a stable solid form using the crystal structure prediction (CSP) method, 3) compute the thermodynamic solubility of diverse chemical structures and solubility enhancement via organic cosolvents using free energy perturbation (FEP+) method, 4) screen polymer excipients that can interact strongly with the API and reduces the risk of recrystallization, and 5) calculate apparent pKa values of ionizable lipids and simulate tthe self-assembly and structural properties of lipid nanoparticles.
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Bio:
Shiva Sekharan is the Global Portfolio Leader (GPL) for Formulations and CSP software at Schrodinger, Inc. in NY. Shiva is responsible for defining the buyer’s journey, go-to-market (GTM) strategy and driving the business development efforts of the MatSci-Formulations software suite across the globe. Shiva is an experienced business development executive in the CRO and AI-based services and software solutions industry and has several years of experience in managing business accounts, customer relationships, in the pharmaceutical and agrichemical industries across the US, Europe and Asia territories.
Before arriving at Schrodinger, Shiva held a BD role at XtalPi Inc., in Boston where he led the US solid-state services unit, worked with departmental heads to establish effective goals, sales targets, outline procedures and best practices and provided strategic directions to increase revenue.
Shiva earned his Ph.D. in Theoretical Chemistry from the University of Duisburg-Essen in Germany followed by postdoctoral stints at the Max-Planck Institute for Polymer Science, Emory University, Fukui Institute for Fundamental Chemistry and Yale University. Shiva is an accomplished computational chemist, with strong research expertise in the areas of quantum chemistry, drug discovery and drug formulation areas (>40 publications, >1700 citations, H-index = 24).
Shiva Sekharan is the Global Portfolio Leader (GPL) for Formulations and CSP software at Schrodinger, Inc. in NY. Shiva is responsible for defining the buyer’s journey, go-to-market (GTM) strategy and driving the business development efforts of the MatSci-Formulations software suite across the globe. Shiva is an experienced business development executive in the CRO and AI-based services and software solutions industry and has several years of experience in managing business accounts, customer relationships, in the pharmaceutical and agrichemical industries across the US, Europe and Asia territories.
Before arriving at Schrodinger, Shiva held a BD role at XtalPi Inc., in Boston where he led the US solid-state services unit, worked with departmental heads to establish effective goals, sales targets, outline procedures and best practices and provided strategic directions to increase revenue.
Shiva earned his Ph.D. in Theoretical Chemistry from the University of Duisburg-Essen in Germany followed by postdoctoral stints at the Max-Planck Institute for Polymer Science, Emory University, Fukui Institute for Fundamental Chemistry and Yale University. Shiva is an accomplished computational chemist, with strong research expertise in the areas of quantum chemistry, drug discovery and drug formulation areas (>40 publications, >1700 citations, H-index = 24).
Presentation 3
Mechanistically Informed Predictive Platforms for Modified-Release Tablet Products: Enabling Strength Scaling and Risk-Based Development
Prof. Jie Shen, Northeastern University
Abstract:
Modified-release (MR) tablet products present ongoing challenges in generic drug product development, particularly when establishing strength scaling and demonstrating bioequivalence without additional in vivo studies. This presentation will discuss mechanistically informed predictive platforms that integrate advanced dissolution methodologies with formulation design principles to predict product performance and enable risk-based development strategies.
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Bio:
Dr. Jie Shen is currently an Associate Professor in the Department of Pharmaceutical Sciences at Northeastern University. She earned her Ph.D. in Pharmaceutical Sciences from the China Pharmaceutical University, Nanjing, China. Dr. Shen’s research centers on the development and mechanistic evaluation of complex drug formulations, bioequivalence assessment strategies, and in vitro-in vivo correlation (IVIVC). Her work spans a range of advanced dosage forms, including nanocarriers, long-acting injectables and implants, locally-acting semisolids, and modified-release oral formulations, addressing challenging therapeutic areas such as cancer, infectious diseases, substance use disorders, and ocular diseases.
Dr. Shen has received several prestigious honors, including the IPEC-Americas Foundation Emerging Researcher Award and the 2025 Gerald Schumacher Pharmacy Faculty Award. She previously serves as Chair of the AAPS In Vitro Release and Dissolution Testing (IVRDT) Community and the Controlled Release Society (CRS) Diversified Products: Delivery Beyond Pharma (C&DP) Division. She serves on the editorial boards of several leading peer-reviewed journals and is currently Chair for the DRPI (Dissolution Research Presentations International)-Americas competition.
Dr. Jie Shen is currently an Associate Professor in the Department of Pharmaceutical Sciences at Northeastern University. She earned her Ph.D. in Pharmaceutical Sciences from the China Pharmaceutical University, Nanjing, China. Dr. Shen’s research centers on the development and mechanistic evaluation of complex drug formulations, bioequivalence assessment strategies, and in vitro-in vivo correlation (IVIVC). Her work spans a range of advanced dosage forms, including nanocarriers, long-acting injectables and implants, locally-acting semisolids, and modified-release oral formulations, addressing challenging therapeutic areas such as cancer, infectious diseases, substance use disorders, and ocular diseases.
Dr. Shen has received several prestigious honors, including the IPEC-Americas Foundation Emerging Researcher Award and the 2025 Gerald Schumacher Pharmacy Faculty Award. She previously serves as Chair of the AAPS In Vitro Release and Dissolution Testing (IVRDT) Community and the Controlled Release Society (CRS) Diversified Products: Delivery Beyond Pharma (C&DP) Division. She serves on the editorial boards of several leading peer-reviewed journals and is currently Chair for the DRPI (Dissolution Research Presentations International)-Americas competition.
