Henry Lin is a 20+ years veteran in the biopharma industry with deep subject matter expertise in the upstream space as well broad CMC experience spanning pre-clinical to FIH to commercial. He is currently the Global Head of Cell Culture Process Development at Sanofi having responsibilities for all of upstream development in U.S. and France. Prior to Sanofi, Henry was an Executive Director in Biologics Process Development at Merck, Director of Cell Culture Development at Boehringer Ingelheim, and Principal Scientist at Amgen. Henry is a co-inventor on 10 patents and an author of 28 peer-reviewed publications. Henry received his PhD in biochemical engineering from Rice University and bachelor’s degree in bioengineering from UC San Diego.

Process Development Scientist at Biogen for 26 years. Coordinate purification development activities for antibodies, fusion proteins, and other types of biological therapeutics. Lead for purification strategy, process development for internal and external programs, process characterization, process validation activities and related global regulatory filings.

Spencer is an NCSU graduate with a B.S. in Polymer and Color Chemistry. His first job was working on the Biogen manufacturing floor alternating between upstream and downstream activities, until migrating into a development support role and expressing interest in upstream media development. He currently works with the cell culture development teams at Biogen, providing chemical expertise to investigations and new media formulation construction. As of 2024, Spencer has returned to NCSU to pursue a Masters in Polymer Chemistry exploring the possibility of bringing advances in polymer science into the media design space.

Abstract

Large scale media production presents various challenges to modern bio-pharmaceutical companies. Highly concentrated feed solutions must be constructed with extra care to ensure the solution remains viable throughout a campaign. A 5L small-scale model was designed to capture comparative mixing stress, temperature profiles, and exposure times to reliably replicate precipitation seen during a production run. The data from the scale down model was used to redesign the formulation and process parameters to produce a stable solution at all scales. In addition to automation parameters, floor operations were evaluated for gains in operational efficiency. Implementation of the observed process changes resulted in successful subsequent media preparation and confirmation of the scalable design.

- Diploma in Biotechnology at Technical University Braunschweig, Germany
- PhD (Dr. Ing.) in Juergen Hubbuchs group at Research Center in Juelich, Germany
- 2007-2012: Development scientist at Novo Nordisk A/S, Denmark
- > 2012: Principal Scientist in Manufacturing Science and Technology department at Novo Nordisk Denmark

Abstract

Continuous capture chromatography is a chromatographic technique promising higher resin utilization, higher productivity and lower buffer consumption than traditional batch chromatography capture processes. The design of cSMB processes is more challenging than the design of batch processes because each chromatographic phase is influenced by the load from the previous phase and it is in turn influencing the subsequent phase. An iterative approach can be used to identify optimal conditions.

Our current process design approach is based on single column breakthrough curves and feeding the breakthrough data into a model. However, this concept leads to a sub-optimal process as it is based on a single loading flow rate, while effectively, flow rates and amounts in interconnected and parallel load phases can be different so using one flow rate only leads to sub-optimal processes.

We are presenting an approach where different flow rates and different breakthrough curves are used as model inputs together with pressure-flow dependencies for the resin of interest. Using the improved process design, significantly higher productivities were achieved than with the standard procedure. The effect was confirmed for a given in-house model process (mAb purification using Protein A affinity chromatography) where productivities could be increased by approximately 50% which has been confirmed at laboratory scale. The presented approach was also evaluated using mechanistic modelling. Finally, implications of the new cSMB design approach for large-scale manufacturing are discussed.  

I joined Pfizer in 2013. Currently, I am a Senior Principal Scientist in Pfizer and lead multiple analytical programs from early stage (before clinical study) to commercial approval. My expertise is focusing on analytical strategies such as process characterization and comparability studies. I successfully developed comparability strategies for multiple programs to support clinical or commercial material usage after manufacturing process changes. In addition, I am a key lead/member of analytical comparability expert group in Pfizer to review analytical comparability strategy for biologics. Before joining Pfizer, I received a Ph.D degree in Northeastern University focusing on characterization of post-translational modifications in biologics using LC/MS.

Abstract

During product development and the life cycle of commercial product, process changes are inevitable. Products manufactured using pre- and post- process changes are required to demonstrate comparability per ICH Q5E. A comparability study is a critical tool to ensure the “similarity” of product quality including efficacy, pharmacokinetics, safety and immunogenicity.
In this presentation, a phase appropriate risk assessment of process change will be introduced, followed by comparability strategy based on the risk assessment. Analytical comparability study tools will be discussed including the analytical comparability plan, comparability criteria, execution, data analysis and presentation. Furthermore, non-clinical and clinical comparability will be briefly discussed if there is any analytical difference which may impact product quality. Two comparability case studies will be presented. The first study is to support a new bioprocess to improve product quality and to facilitate larger scale manufacturing. The second one is to support the use of an early process stability study for establishing commercial shelf-life by leveraging the comparability study for an accelerated program. The agency feedback from multiple markets for the case studies will also be discussed.  

Kyle McHugh received his PhD in Chemical and Biological Engineering from the University of Delaware on cell culture models of neurodegenerative disease. He worked in Upstream Biologics Process Development at Bristol Myers Squibb supporting commercial process development and process characterization for multiple therapeutic protein projects as well as high-throughput optimization, media development, and PAT advancement. Currently, Kyle is an Associate Director of Biotherapeutics Process Development at Takeda Pharmaceuticals in Lexington, MA supporting advancement of platform capabilities and multiple project modalities from FIH through late-stage development.

Abstract

Speed to clinic is critical to enable faster identification of successful early-stage drugs and subsequently faster BLA filing to commercialization. Here we discuss multiple levers to enable faster timelines to IND balancing prioritization of the timeline, business risk, and minimization of pre-investment. From a cell line perspective, strategic choice of source material for exploratory and GLP Tox studies allows for earlier delivery of representative material. Application of automation tools for screening clones and sampling bioreactors allows for more extensive screening and efficient process development. Data connectivity for automated capture and visualization of real-time offline and online process metrics helps to monitor ongoing runs, aggregate data for modelling, and streamline analysis for reports and regulatory submissions. Overall, we demonstrate significant improvements in speed, throughput, and automation for early-stage cell culture process development.

Angela joined Bristol Myers Squibb in 2013, and over time has taken on increasing responsibilities. Angela and her team of 4 direct reports and 16 full-time employees are responsible for downstream process development for BMS’ growing biologics pipeline. Angela is currently doing a 6-month rotation, where she leads an additional team of 4 direct reports and 28 full-time employees responsible for process analytics. She has been the drug substance matrix lead since 2016 for a monoclonal antibody asset, from mid-stage through commercialization and life cycle management. She has driven several key strategic cross-functional initiatives. Prior to BMS, she worked for several years at Sanofi Genzyme and Regeneron Pharmaceuticals, and has a total of over 19 years of industrial experience. Angela holds M.S. and Ph.D. degrees in Chemical and Biomolecular Engineering from the University of Maryland, and a B.S. in Chemical Engineering from the University of Virginia.

Scott H. Altern completed his PhD in Chemical Engineering at Rensselaer Polytechnic Institute (RPI) in Professor Steven Cramer's group (2023). His PhD research was centered on advancing the state-of-the-art in mechanistic modeling of preparative column chromatography and high-throughput process development. Currently, his role is at AbbVie in Worcester, MA where he focuses on mechanistic modeling of preparative chromatography, computational fluid dynamics, and molecular modeling to directly aid in process development. Scott is also highly involved in using high-throughput (HT) experimentation to expedite process development and model generation. Overall, Scott aims to transform industrial bioprocess development using automation and multiscale modeling.

Abstract

In this work, we employed HT instrumentation and multiscale modeling to support early-stage process development for two monoclonal antibodies (mAbs). These molecules had nearly the same isoelectric point but were found to behave differently in CEX. Both antibodies were screened for their dynamic binding capacity (DBC) on POROS XS RoboColumns across a wide range of loading pH and conductivity. Curiously, opposing DBC trends with loading pH and conductivity were observed between the mAbs. Generally, increasing loading pH and conductivity, beyond a certain threshold, results in a reduced DBC in CEX systems. This was consistent with the behavior for mAb B, but contradicted the trends seen for mAb A. Molecular modeling was then employed to identify the molecular features responsible for the unusual screening results. Homology models were constructed for each of the antibodies, from which molecular descriptors were calculated. Descriptors such as net charge and CDR positive charge surface area were correlated with DBC values to confirm that the degree of exclusion was directly related to molecular charge properties. Additionally, mechanistic model parameters were regressed from RoboColumn breakthrough curves to provide additional insight into the unique chromatographic behavior. For instance, trends in surface diffusivity with loading conductivity and pH were examined to determine their contribution to CEX loading performance. Further experimental studies were conducted in order to elucidate the molecular origins of the observed trends. Dynamic light scattering (DLS) experiments were carried out to determine the protein-protein interaction constant over a range of pH and conductivity. Interaction strength was found to be correlated with DBC, suggesting that repulsive forces negatively affect DBC and are dependent on solution conditions. In summary, this study used high-throughput screening and multiscale modeling in conjunction to facilitate CEX PD and improve process understanding from a molecular perspective. 

Daryl is a director in the Bristol Myers Squibb MS&T drug substance group. He leads a team of process subject matter experts and laboratory scientists supporting the BMS commercial and registrational biologics portfolio. He has a PhD in chemical engineering and 18 years experience in the biotechnology industry.
 
Abstract
A commercial process was experiencing significant yield and product quality variability. To enhance process SME insights, the enterprise invested in a substantial modelling effort, which included building a data pipeline and conducting both non-proprietary and proprietary process modelling efforts. Initial observations confirmed SME insights and provided quantified targets for future batches. The work for this project highlighted opportunities to improve data quality through addition of PAT tools, expanded data sources, and incorporation of more mechanistic features into models.

Dhanuka Wasalathanthri, Ph.D. is an Associate Director at Bristol Myers Squibb (BMS) where his main role is to serve as the strategy lead for Process Analytical Technology (PAT), High-Throughput Automation and Digital Capabilities at Analytical Development organization. He is a versatile leader with more than 10 years of biotech industry experience in strategic implementation of technologies, CMC analytical development, project management and building high-performing teams in meeting corporate goals. Dhanuka represents BMS at several academic and industry consortia, and he Holds a Ph.D. in Analytical Chemistry from University of Connecticut, USA.

Abstract

Biopharmaceutical landscape is getting increasing complex with a broad spectrum of modalities to meet unmet medical needs, while external geo-political, regulatory, generic competition and sustainability challenges demand acceleration of drug development pipelines. Despite significant advancements in the development and manufacturing workflows for faster and more efficient processes such as intensified and continuous operations, in-process analytical testing is still mainly centric around manual testing. Harnessing the power of automation enables fit-for-purpose and plug-and-play modular units with end-to-end parallel testing of multiple analytical assays with significant improvement in speed and productivity. The concept of robotic process automation (RPA) tools resembles as “Digital Workers” to automatically process, analyze and transcribe analytical results in real time which greatly improves the rapid availability of data to the stakeholders. Complex modalities in the pipeline often require non-platform analytical methods, unique approaches, and upfront investment on method development. Such efforts can be greatly simplified using data interrogation techniques such as Machine Learning and Deep Learning approaches. Herein a vision, roadmap and case studies of automation and digital transformation efforts measured against main KPI’s such as speed and productivity for in-process analytics for biologics are presented.

Shyam Panjwani is currently working as a Principal Data Scientist at Bayer Pharmaceuticals, Berkeley. He holds a PhD degree from University of Houston and a bachelor’s degree from Indian Institute of Technology, Kanpur in chemical engineering. He has 8+ of experience in applying AI/ML/statistics for biologics manufacturing processes, biological assays and process development. Before joining Bayer, he worked with Halliburton and Air Products.

Abstract

The biopharmaceutical industry is increasingly turning to data science to optimize manufacturing processes and enhance product quality. This presentation explores two innovative case studies that leverage data science to improve efficiency in biologics manufacturing.

The first case study focuses on a cloud-based predictive modeling application designed to enhance the predictability of mammalian cell culture processes. By forecasting bioreactor potency from at-line process parameters over a multiple-day horizon, this application quantifies prediction uncertainty and provides valuable insights for process control, ultimately enhancing productivity and product quality.

The second case study presents a novel framework for assessing the out-of-specification (OOS) risk associated with drug product potency. This framework utilizes a cloud-based statistical software application to streamline the evaluation of alternate potency targets, reducing manual errors and improving the efficiency of risk assessments.

Together, these case studies illustrate the transformative impact of data science on the biopharmaceutical manufacturing landscape. 

Jian Ren is a Principal Scientist in the Manufacturing Science and Technology (MSAT) organization at AbbVie Bioresearch Center in Worcester, MA. She leads process tech transfer, validation, and manufacturing support activities for the downstream manufacturing process for late phase and commercial biologics programs, including monoclonal antibodies, bispecific antibodies and fusion proteins. Prior to MSAT, she led purification process development, tech transfer, and validation activities for early-to-late-phase biologics projects. Prior to AbbVie, her research focused on the development and optimization of polymeric membranes and modules for various separation applications. Jian holds a PhD in Chemical Engineering from the University of Connecticut.

Dr. Lihua Yang has been at AbbVie for over 20 years and is currently a Senior Principal Research Scientist and Group Leader within the Purification Development Department in Product Development Science and Technology organization. Dr. Yang led 25+ early and late phase programs and numerous innovation initiatives on biologics in areas of mAb, bispecific, antibody-drug conjugates and gene therapy. She has extensive experience in bioprocess development, characterization, validation, tech transfer to global GMP manufacture, process internalization, product launch, IND and BLA filing. Besides leading strong functional and cross-functional teams to deliver innovative and improved purification process, Dr. Yang is also a subject matter expert in bioanalytical assay development and protein characterization. Dr. Yang holds a Ph.D degree in Chemistry and Chemical Biology from Northeastern University, and a BS degree in Physical Chemistry from Peking University in China.

With over 35 years of professional experience in the biopharma industry, his experience includes serving as the Technical Head of the biologicals franchise within Resilience, overseeing all activities related to recombinant protein and monoclonal antibody projects and technology development initiatives.

Prior to that, Tom has held various C-suite positions at other biopharma companies such as 4th Dimension Bioprocess, Inc. (co-founder and COO), BioFlash Partners (President and CEO), TranXenGen, Inc. (Vice President, Operations), Dyax Corp (Vice President, Program Management and Product Development).

Tom is a regular speaker at leading biopharma conferences, has published over 20 academic articles and has also developed and filed several patents, related to innovative biopharma manufacturing processes.

Tom holds a B.S in Chemical Engineering from MIT and a M.S. in Chemical Engineering from the University of California, Berkeley.

Ishaan Shandil is a Biochemical Engineer with 16 years of process and product development, manufacturing and project leadership experience in the Biopharmaceutical industry. Ishaan currently works as an Associate Director Process Engineering MSAT Bristol-Myers Squibb (BMS) where he is responsible for leading projects in Process Engineering and Manufacturing Technology functions. In his role at BMS, he also manages the technical aspects of an external manufacturing relationship and provides CMO oversight. Prior to joining BMS, Ishaan worked for Merck & Co. in roles spanning across R&D and Manufacturing where he designed and developed scalable bioprocesses to manufacture differentiated biologics drug candidates. He also studied and published the impact of process conditions on Glycosylation in monoclonal antibodies and fusion proteins. Ishaan received a MS in Chemical & Biochemical Engineering from University of California Irvine and an MBA from University of Chicago Booth School of Business.

Ravi Vattepu is a Senior Scientist in the Pivotal Biologics Process Development group, focused on purification process development of biologics. He holds a PhD in Chemistry from Wichita State University and completed postdoctoral research at Harvard Medical School, where his work focused on antibody glycosylation and the role of IgG subclasses in inflammation.