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L. Letendre et al.

13.10 Conclusions

The story of the reintroduction of Mylotarg into the US market is one of looking back at what was developed and what data were available at the time to support the registration. This analysis provided Pzer the opportunity to apply the many scien­tic advances, which had occurred since the original ling. These advances touched the science that is used in characterizing both processes and products and allowed Pzer to provide a control strategy containing a deeper understanding of the produc­tion and control of Mylotarg than was available originally.
Re-developing/investigating a drug manufacturing process with a strictly dened set of drug product attributes and, in this case, with a 17-year history, is different than dening a process as a drug goes into a Phase 1 study and rening it during clinical development. The Mylotarg drug substance is an antibody-drug conjugate; however, it was the drug substance intermediates, gemtuzumab and activated cali­cheamicin derivative, that beneted from manufacturing optimization. The interme­diates could be more easily modied to address their shortcomings while maintaining the nal drug substance quality.
Just as Pzer had no experience returning a nearly 20-year-old drug to market, neither did the regulatory agencies. They were receptive to the possibility of a more advanced intermediate as a regulatory starting material than originally led for acti­vated calicheamicin derivative. While the FDA and EMA did ultimately require visibility of the intermediate’s manufacturing process back to the bacterial cell bank, still other global health authorities accepted N-acetyl calicheamicin as a regu­latory starting material.
The multiple functions that worked on all aspects of the re-registration of Mylotarg ultimately succeeded and it was returned to the US market on September 1, 2017, over 17years after it was originally approved and almost 7years after it was voluntarily withdrawn. Mylotarg continued the regulatory success with approv­als throughout the world including Europe where it had been rejected in prior years. Mylotarg’s re-introduction in the US and introduction in other major markets offered a new alternative treatment to AML patients globally to manage this devas­tating disease.
Acknowledgements With a project that has spanned two decades, it is impossible to acknowl­edge all those who have contributed to bringing Mylotarg to patients. Similarly, the number of colleagues who have contributed to just the effort to return Mylotarg to the market is also quite large. We acknowledge the following colleagues while realizing that the credit goes well beyond this list.
Pzer Global Medicine Team who shepherded the vast amount of clinical data that allowed the rest of the team to pursue their chemical/biochemical specialties as their contributions.
Contributors to the calicheamicin process development portion of the project include: Nataliya Bazhina, Eric Bortell, Lawrence Chen, Joe Collins, Robert Dugger, Brad Evans, Stephen Freese, Xi Hu, Julius Lagliva, Mark Maloney, Jim Mo, Vimal Patel, Amar Prashad, Wesley Swanson, April Xu, Chunchun Zhang, Sen Zhang.
Contributors to the antibody portion of the project include: Daniel Boisvert, William Daniels, Mary Denton, Chris Gallo, Heyi Li, Joshua Ochocki, Mary Switzer.
13 Mylotarg: TheJourney toFDA Reapproval andBroad International Approval
401
Contributors to the drug substance portion of the project include: Eric Bortell, Brooke Czapkowski, Brad Evans, Qingping Jiang.
Contributors to the drug product portion of the project include: Bakul Bhatnagar, Serguei Tchessalov, Nathalie Hayduk, who led the joint Pharmaceutical Sciences and Pzer Global Supply development teams.
Contributors to the calicheamicin, drug substance, and drug product manufacturing include: Pzer Global Supply Pearl River—Technology team, QA team, QC team, Tok Han, Michael Hripcsak, David Merkooloff, Sonal Shah, Paramanathan Guruparan.
Contributors to the antibody manufacturing include: Pzer Global Supply Andover Team.
Contributors to the CMC regulatory lings include: Jana Cook, Caroline Kinross, Jacqueline Macaulay, Jacklyn Moxham, Pzer Global CMC team.

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Part VII
Biopharmaceutical Informatics
and Analytics
Chapter 14
Biopharmaceutical Informatics: AStrategic Vision forDiscovering Developable Biotherapeutic Drug Candidates
JoschkaBauer, SebastianKube, PankajGupta, andSandeepKumar
Abstract Biotherapeutics are rapidly emerging as a successful class of pharmaceu-
ticals, even though signicant challenges to their discovery and development remain. In this book chapter, we establish a conceptual framework for potential computational interventions at every stage of biologic drug discovery and early development. We call this framework biopharmaceutical informatics. This chapter provides a comprehensive overview of our strategic vision. This vision calls for closer collaboration between drug discovery and development functions of biophar­maceutical industry by integrating the considerations of developability during early stages of drug discovery. Computational tools already available to enable biophar­maceutical informatics are reviewed in this work. While our focus is on monoclonal antibody-based biologics, the concepts discussed in this work are also applicable to novel formats such as multispecic biologics.
Keywords Biotherapeutics · Drug design · Drug development · Developability · Function · Computation · In silico · Bioinformatics
J. Bauer · S. Kube Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
P. Gupta In Silico Team, Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgeeld, CT, USA
S. Kumar (*) Molecule Design and Modeling, Computational Science, Moderna Therapeutics, Cambridge, MA, USA e-mail: Sandeep.Kumar@modernatx.com
K. Gadamasetti, S. A. Kolodziej (eds.), Bioprocessing, Bioengineering and Process Chemistry in the Biopharmaceutical Industry,
https://doi.org/10.1007/978-3-031-62007-2_14
405© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
406
J. Bauer et al.

14.1 Introduction

14.1.1 Growing Demand forImproved Developability
ofBiotherapeutics
Biotherapeutics, especially, the monoclonal antibodies (mAb) have now been indus­trialized. This success is reected in the fact that the load in the development pipe­lines of the biopharmaceutical industry is ever increasing with more than 800 antibody therapeutics in the early-stage and 115in the late-stage phases with an average R&D cost to launch per project of about two to four billion US dollars [1]. While we celebrate rise of monoclonal antibodies as the fastest growing class of biotherapeutics (or biologics as they are often referred as), one should, nevertheless, recall that the chance for a monoclonal antibody to be approved for marketing is about 5–10% [2]. Improved molecular understanding of the new drug and t to the engineering and development platforms at preclinical R&D stages might increase this chance of success [3] and therefore contribute towardseven greater success of biologics. This desire to improve the rate of translation of biologic drug candidates into drug products available in the market is often summarized under the term “developability,” which describes the feasibility of a drug candidate to successfully progress from discovery to clinical drug product development, and canbe traced as early as the rst decade of the 2000s [4, 5]. The developability concept is based on experience gained from previous Chemistry, Manufacturing, and Control (CMC) development programs [6], along with the realization that several of the CMC issues encountered during the development stages could have been mitigated via minor primary sequence modications during discovery.
14.1.2 Developability Issues Arising fromUnfavorable
Biophysical Properties
In comparison to small molecule drug products, biologics are more complex and het­erogenous in their three-dimensional structures and physicochemical properties, which are highly dependent on their respective manufacturing process [7]. A subset of those properties is relevant for drug efcacy and safety and therefore needs to be monitored and controlled [8]. They are referred to as Critical Quality Attributes (CQAs) [9]. As manufacturing processes for biologics are generally more complex and have a greater inuence on the biophysical properties of the active pharmaceutical ingredient (API), the regulatory bar for drug manufacturers to demonstrate their abil­ity to make biologic drug products via well controlled, safe, and reproducible pro­cesses is much higher in comparison to that for the small molecule drug products [10].
In an ideal biologic drug product development program, an excellent mAb can­didate with optimal biophysical properties and a perfect t to standard development platforms would be reproducibly expressed and puried with a high yield and
14 Biopharmaceutical Informatics: A Strategic Vision for Discovering Developable…
product quality resulting in low production costs. Subsequently, the active pharma­ceutical ingredient would be formulated in liquid form with high chemical, confor­mational, colloidal, and physical stability during shipping and storage over its shelf life and would be easily administrable at the concentrations required for effective dosing [11]. However, the real circumstances of the biopharmaceutical industry deviate substantially from this idealized state, since the majority of lead candidates experience development issues that are related to their unfavorable biophysical properties, but the specic reasons behind a drug failure in the development phase remain often poorly documented and therefore not fully understood [4]. Increasing the complexity, next-generation drugs of multi-specic mAb derivatives tend to have even greater CMC hurdles [12, 13]. Additionally, the quality target product prole dening the key design requirements of the nal drug product is not fully dened in the early development stages [14]. The therapeutic dose and regimen for clinical studies are not yet dened and need to be established in preclinical experi­ments with the same material that is used in the clinical studies [15]. The anticipated administration route might change during the development as well as the primary packaging and/or the inclusion of a medical device [16]. All these design choices bring different and additional requirements on different physicochemical properties of the biologics and the formulation that can also modify the platform manufactur­ing processes. Examples are solution viscosity, resistance to shear stress, interaction on air: water and interfaces, compatibility to a liquid or lyophilized formulation, compatible excipients, and active pharmaceutical ingredient (API) concentration. Therefore, the biopharmaceutical industry aspires to meet the increasing demand for modern biotherapeutics by improving their developability via rational design.
Table 14.1 summarizes the most commonly occurring issues in formulation development of a liquid formulation for parenteral application.
407
14.1.3 Biopharmaceutical Informatics: AnIntegrated
Approach toDiscovery andDevelopment ofBiotherapeutics
As described above, biotherapeutic drug candidates often face several developabil­ity issues and therefore warrant greater scrutiny from the regulatory agencies. Remarkably, however, each of the development risks described above can be traced back to the intrinsic physicochemical properties of the drug candidate encoded in its primary sequence and three-dimensional structure [6], both of which are speci­cally targeted by the promising approach of biopharmaceutical informatics. Kumar etal. previously dened this emerging eld as any computational effort geared to advance efcient and cost-effective translation of biologic drug candidates into drug products [17]. Figure14.1 describes the motivation for biopharmaceutical informat­ics. This interdisciplinary area explicitly covers both in silico tools alone and in combination with experimental studies such as developability assessment.
408
Table 14.1 Major challenges and opportunities for application of computation at various stages of antibody-based biotherapeutic drug discovery and development
Opportunities for computational
Stage Major challenge(s) In vitro
production of immunogen(s)
Antibody generation
Hit selection and lead identication (LI)
Lead optimization (LO)
Early stage developability assessment
1. Immunogen proteins may have unknown structure with varying degree of sequence homology with proteins of known structures
2. Protein insolubility and aggregation leading to low material yields
1. Antibody generation by immunizing animals is costly, time consuming, yields variable results, and may require humanization
2. Humanized mice do not capture fully human immune repertoire
3. Phage and yeast display technologies can rapidly identify binders but their developability may need further optimization
1. Sequencing of the hits
2. Epitope mapping of the hits to assure desirable therapeutic effect in absence of structural models for Ag:Ab complex
3. Experimental testing of several hundreds of hits for function and developability can be time and resource consuming
Identied lead candidates may require humanization (if from a nonhuman source such as wild type mouse), afnity optimization, and removal of physicochemical liabilities for improved developability
1. Molecular stability and tness of the drug candidate nominated by drug discovery to platform processes used in drug development
2. Dynamic development targets
applications
1. Protein structure prediction
2. Sequence/structure-based optimization for improved conformational stability andsolubility can help improve quantity as well as quality of material availablefor immunization
1. Development of computational techniques
analogous tovirtual screening of human antibody repertoires can lead to rapid identication of binders. It can also potentially alleviate the need to produce immunogens in large quantities
2. Computational design of phage and yeast display libraries for improved developability
3. Computational redesign of antibodies with good developability to binding different specicities
4. In silico generation of antigen-specic/
antigen-agnostic antibody libraries via articial intelligence and machine learning
1. Development of appropriate Sanger and NGS sequencing pipelines
2. Computational prediction of epitopes and paratopes
3. In silico assessments of hits for developability and manufacturability can guide selection of developable hits and identify lead candidate(s) with good developability
Structure-based modeling of the lead candidates can guide their humanization, afnity maturation, and identication of potential sequence/structural motifs that may drive their physicochemical degradation. Availability of this information can help guide protein engineering strategies for lead optimization (LO)
1. Multiscale molecular and mathematical simulations to predict t to platform and understand molecular response to stresses faced during manufacturing, storage, and shipping
2. Development of predictive algorithms for identifying the appropriate bioprocess conditions and formulation ingredients for drug product development
3. Decision-support systems
J. Bauer et al.
(continued)
14 Biopharmaceutical Informatics: A Strategic Vision for Discovering Developable…
409
Table 14.1
Stage Major challenge(s) Process and
drug product development
Clinical development
All stages Numerous challenges related to
(continued)
1. Up-scaling
2. Comparability
3. Customization of processes for manufacturing (bioprocess), formulation development, and analytical characterization
1. Trial design and criteria for patient recruitment
2. Safety, efcacy, and pharmacology
function and physicochemical stability of the biotherapeutic drug candidates
Opportunities for computational applications
Machine learning and mechanistic modelling techniques to model bioprocesses conditions and yields, and development of digital twins
1. Biostatics and genomics technologies
2. Immunogenicity predictions and their clinical relevance
3. Safety and toxicology data capture and analyses
4. Modeling and simulation of pharmacology of drug candidates
1. Data capture into databases that can be easily analyzedby following FAIR (Findable, accessible, interoperable, and recyclable) principles
2. Data analyses and creation of digital tools to predict experimental outcomes
3. Biopharmaceutical informatics
Fig. 14.1 Motivation for biopharmaceutical informatics
The correlation of “macroscopic” experimentally determined attributes of a bio­logic with its “microscopic” sequence-structure aspects computed in silico is the major unsolved problem in biopharmaceutical informatics. Depending on the amount of data available, several statistical as well as machine learning approaches can be applied to obtain mathematical models capable of predicting solution behav­iors of monoclonal antibodies solely from their sequence-structure information [1826]. Below, we stitch together a diverse set of concepts in bioinformatics, molecular modelling, and simulations along with the experiments to demonstrate practical feasibility of our vision of biopharmaceutical informatics and Table14.2 lists several opportunities to apply computation at every stage of biopharmaceutical drug discovery and development. However, the eld has not matured uniformly in