Scientists and researchers have made remarkable breakthroughs in basic science to better understand the complex pathology of many diseases today. Some of that progress is through utilizing technology to uncover new insights, and some of the progress is through the incalculable capacity of the human mind to analyze problems and find solutions. With all that knowledge and all the effort put in, however, human biology and disease pathology continue to be surprising. Every day researchers continue to make profound discoveries in basic science that create new possibilities in fighting disease.

A conundrum exists, however: the more knowledge that is accumulated about a disease, the harder it becomes to incorporate all that data to understand the disease complexity, and, ultimately, the more difficult it is to find treatments. Complex diseases require complex solutions to untangle the disease biology because human biology is interconnected — millions of signals and cellular interactions are taking place each second. That said, a better understanding of that complexity also creates an opportunity to attack the disease in different ways and explore new insights that could treat or cure many of today’s complicated and hard-to-treat diseases.

To untangle that disease complexity, scientists are using technology to their advantage. Modern computational approaches can allow them to analyze disease data collected over decades in ways that were never previously imagined. A significant drive has been placed on artificial intelligence (AI) as a supplement to how scientists take that data and screen libraries of drug candidates against it to find new potential therapies.

Many companies are doing just this, but what they are effectively achieving is an acceleration of the traditional research and discovery process. At its simplest form, the traditional drug discovery process involves taking a single disease hypothesis, such as a widely known or novel disease target or the phenotype of a specific disease, and trying to develop or discover new molecules that can modulate it. Computers utilizing AI can do this more efficiently, enabling researchers to accelerate screening efforts for new therapies. There is little debate about the utility of AI here.

However, a fundamental problem remains. Researchers are targeting diseases through one or two known characteristics of that disease, whether it’s a specific receptor or a more complex disease pathway. The researchers can target several receptors, for example, but currently the industry is doing that in a silo. Typically, the researcher targets a specific receptor, gathers insights and data, then targets another receptor. This activity continues on down the line until the researcher understands how a molecule or drug interacts with that disease.

Imagine instead, however, targeting the whole disease and understanding how any particular molecule or drug simultaneously modulates all those receptors. Or better yet, building a complete model of a disease and analyzing a potential molecule or treatment against the complex inner workings of that disease, from receptors to ligands to other disease signals. The only true way to do that definitively is through human studies, but the efficacy and safety of therapies must first be explored before even approaching clinical research. That early exploration is the phase where disruptive technology can have a significant impact and bring more potential treatments to the clinic.

It is at that early exploratory phase where the value of AI prevails. The next wave of innovation in AI must examine how all the puzzle pieces of human biology are connected, especially as scientists uncover more and more about disease pathology. This level of drug discovery has the potential to uncover new therapies and new mechanisms of action that target disease more safely and effectively, and this breakthrough is possible today.

In recent years, a team working with AI has been creating 

 models of a disease with dozens of heterogenous data types that can be used to screen databases of drug and drug-like molecules against that disease. Effectively, the team has been looking at how any given molecule will interact with human disease biology from dozens of different angles at the same time. This work is the closest to mimicking human trials in early preclinical research that has ever been observed, and it is work that could not be accomplished without the use of technology — in particular, AI.

Two critical lessons stand out in the team’s work. The first is that, by building these models, the researchers are not only rediscovering molecules that are known to work, have been tested, and passed human trials, but discovering completely new potential drugs that rank high in efficacy and safety that would otherwise not be found using any single-disease signal. The team’s work is a testament to the vast complexity of human biology and disease pathology. It also showcases the fact that potential treatments are being missed when only one dimension of a disease is examined against potential molecules.

The second lesson that stands out is that, if molecules or drugs can be tested through this approach, then scientists can gain much insight into how those molecules or drugs may work in human trials long before reaching the human clinical trial phase. This accomplishes several important goals, chief among those is de-risking experimental R&D. R&D is an extremely risky business, and researchers have come to accept that, for every successful treatment, billions of dollars will have been spent on thousands of other molecules that fail. Using technology to better model a more complete disease can help to pull those numbers in the right direction. There is no anticipated time for when technology will completely remove risk, but any progress in that direction is good progress.

The above lessons are just two examples of benefits that the bio/pharm industry can seek in drug discovery and development by harnessing technology to understand a more complete picture of any given disease. One thing is for certain, however: diseases will continue to be elusive and unpredictable. For every disease that researchers have found a treatment for, there exists millions of disparate data points. The more that scientists can leverage technology and AI to better analyze those pieces of the puzzle and generate a better picture of the whole disease, the better they will be able to identify and test potential therapies against a more holistic disease hypothesis.

Every barrier and problem that the bio/pharma industry faces has been met with new innovations to address them, and the growing complexity of disease data is no different. The potential of AI and other technologies to give a better understanding of diseases and how any given molecule or drug may affect that disease will be groundbreaking in drug discovery. In a world where knowledge of diseases becomes more complex, the technology could evolve to unravel that complexity. Thus, instead of finding fewer breakthroughs, the industry may see a continued steady rate of new discoveries despite having to deal with exponentially harder problems.

Mark Eller, PhD, is senior vice-president of Research and Development at Aria Pharmaceuticals.

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Advancing the use of AI to understand the whole of a disease can reveal drug development insights that lead to drug discovery breakthroughs.

Technology as a New Evolution in Fighting Disease

An understanding of the pharmacokinetics (PK) of monoclonal antibodies (mAbs) is important so that potential patient responses to treatment can be predicted. Both PK and pharmacodynamic (PD) studies are necessary in the development of a new mAb drug candidate. 

PKPK/PD modelling is already widely used in pre-clinical and clinical drug development; these studies are important for characterizing drug candidates quantitatively as well as aiding go/no-go decisions and informing future trial design and optimal dosing regimens (1). However, what is less well known is that PK/PD modelling can also be an effective tool in the earliest stages of drug development, such as in target selection and lead candidate selection. The same modelling techniques can be used to predict and illuminate the drug candidates that have the necessary characteristics for potentially successful development, or, conversely, can help pinpoint which drug development programmes may be less successful, before too much time and cost are accrued on that specific programme.

In particular, mAbs, which are an important therapeutic class, have complex pharmacology and interdependent PK/PD properties. Understanding the PK and PD of mAbs and their biological activity as well as their mechanisms of action are crucial factors in enabling their design and selection. Understanding these factors allows for the design of appropriate studies to characterize the mAb’s efficacy and toxicity, the translation of PK/PD parameters to humans, and the optimization of a dose and regimen for maximizing success in clinical development (2).

Meanwhile, the practice of characterizing the relationship between PK and PD is an important tool in the drug discovery phase. PK/PD strategies should be implemented in the early phases of research during drug discovery projects because these studies can enable a successful transition from discovery phase to development phase (3). An effective PK/PD study design, as well as proper analysis and interpretation can help researchers characterize the PK–PD relationship. Understanding the relationship would enable understanding of the mechanism of drug action and help identify PK properties that can help improve and optimize the design of the drug compound (3).

To learn more about the necessity of PK/PD studies in early mAb development and the most reliable ways to conduct these studies to generate data compliant with investigational new drug application (IND) filings, Pharmaceutical Technology Europe spoke with Maotian Zhou, PhD, Principal Scientist—DMPK Services Department, at WuXi AppTec.

 In the early development of a new mAb, what role does PK play in the development lifecycle?

 PK plays an important role in the early development of mAb therapeutics. This is even more apparent when the antibody is designed against a novel target, or if the antibody aims to be a ‘best in class’ drug in an already competitive arena with well-studied targets (i.e., HER2, PD1 [programmed cell death protein 1]/PDL1 [programmed cell death ligand 1], etc.).

This may differ from past views on mAb drug development. It is usually assumed that, for humanized monoclonal antibodies, the 

 absorption, distribution, metabolism, and excretion (ADME) characteristics share more significant commonality than small-molecule drugs. Thus, the importance of PK studies in the early phase of mAb development is diminished. The efficacy (or pharmacodynamics) appears to be more significant in the leading-candidate mAb selection.

The importance of PK studies increases with a better understanding of mAb drug mechanisms. Many factors can affect mAb exposure–response relationships 

, the affinity between the mAb and its ligand, the association between the mAb and different Fc receptors, and the modifications and physicochemical characteristics of the mAb. These factors form the basis for the continuous refinement of current mAb drugs. The mAb PK properties act as a bridge between its design philosophy and ultimate efficacy and safety. Fully understanding candidate mAb PK parameters can provide support for leading-mAb drug selection.

In summary, the PK studies significantly impact whether a mAb moves forward, and should happen early in discovery. Pushing a mAb toward an IND submission without fully understanding its PK could waste time and resources and/or result in programme failure.

 I always see the terms PK and PD together when discussing drug candidate screening or in drug discovery. What is the importance of both in mAb development, and is it necessary to have both PK data and PD data (can you have one without the other)?

 Simply put, PK is ‘what the body does to the drug’, whereas PD is ‘what the drug does to the body’.Explicitly, PK quantitatively describes the process of absorption and disposition of the drug in the body. PD evaluates the time course of the pharmacological effects of drugs.

For mAb drug development, the PK and PD always communicate, because mAb drugs generally have long-acting PD effects, and PD will have a direct impact on PK. PK and PD are mechanistically linked (unlike small-molecule drugs). Having both PK and PD data for mAb development is strongly recommended. PK/PD studies serve distinct roles at different stages of mAb drug development.

 What is a ‘best practice’ approach to conducting PK/PD studies, and do these studies necessarily need to be compliant with regulatory standards?

 PK/PD studies should adapt to the different requirements of each research stage. In the discovery phase, the PK/PD assessments facilitate an understanding of the biology by validating and confirming target engagement. For the leading-mAb selection stage, PK/PD studies should help to select the most ‘potential’ mAb, along with efficacy and toxicity evaluations, to minimize the risk of off-targeting. At the IND stage, systematic PK/PD studies, along with safety studies, play a vital role in translating pre-clinical knowledge to first-in-human clinic studies. In the clinical phase, further in-depth PK/PD studies can identify the covariates and potential sources of population variability, establishing exposure–response relationships in different cohorts. And finally, these studies support proper dose selection, regimen optimization, and biomarker tools establishment.

PK/PD studies must comply with regulatory guidance, both during and after the IND phase of mAb drug development. It is also helpful to reference the regulatory standards in the early phase of drug development. Given that regulations are often adapted based on the lessons learned from many actual drug development cases, items rooted in regulatory guidance are often representative of key points in drug development. For example, a mAb PK bioanalysis in the discovery phase, using a partially validated method (validating specific parameters such as selectivity and stability) will make the mAb quantification more reliable in an unconventional matrix.

 What role or impact do PK/PD studies have on an IND filing for a new mAb?Are they even relevant at that stage?

 PK/PD studies are essential materials for an IND filing. PK/PD studies serve the following purpose in the IND phase for mAb development: integrate pharmacology and toxicology data and help correlate pre-clinical knowledge to first-in-human clinical study; bridge toxicology studies to define maximum tolerated dose (MTD) and select first-in-human dose; design a dose escalation scheme; and project efficacious dose in clinical.

 What currently are the major analytical/technical challenges in conducting PK/PD studies on new mAbs?

 Several challenges currently exist within PK/PD studies for mAb drugs. First, the assumptions used to simplify the exposure–response relationships in PK/PD modelling may not always be appropriate. For example, in the past, the PK/PD model assumed the drug concentration in tumor cells was related to the cell-killing effect. However, in the absence of an actual measurement of the distribution cascade, the validity of this assumption is uncertain.

Second, the translation from animals (pre-clinical) to humans (clinical) based on PK/PD modelling is also challenging. Furthermore, existing PK/PD modelling assumptions are usually based on prior experience, as such studies are needed to understand the applicability of new mAbs.

Third, the reliability of PK/PD data depends on the selection of a proper analytical method/platform, which may be a challenge in some instances. The bioanalysis should always follow a fit-for-purpose principle. Based on the ligand properties (such as solubility and endogenous concentrations), the availability of critical reagents, and desired sensitivity, a suitable assay format needs to be designed.

There is an ongoing effort regarding PK/PD analytical strategy optimization. The ability to accelerate research and develop safe and effective mAbs rely less on tools and more on an investigator’s skills, knowledge, and expertise. Drug developers and sponsors that do not have the time, capacity, or expertise to conduct these investigations in-house should consider working with a testing partner.

 77 (5) 740–745 (2014).
2. A.V. Kamath, 

 21–22, 75–83 (2016).
3. T. Tuntland, et al., 

Feliza Mirasol is the science editor for 

PK/PD studies are a crucial tool in the successful development of biologics.

PK/PD Studies Prove Critical for mAb Development

Sosei Heptares announced on Dec. 16, 2022 that it has entered a drug discovery collaboration with Eli Lilly with the goal to discover, develop, and commercialize small molecules that modulate novel G protein-coupled receptor (GPCR) targets associated with diabetes and metabolic diseases.

The agreement will use Sosei Heptares’ StaR technology and structure-based drug design platform and Lilly’s extensive drug development and commercialization expertise as well as its therapeutic area expertise in diabetes and metabolic diseases. Sosei Heptares is eligible to receive $37 million upfront in cash and commercial milestone payments totaling up to $694 million, plus tiered royalties.

“This new agreement with Lilly further reinforces our position as a global partner of choice for GPCR-focused drug discovery targeting major diseases where patients remain in need of new and effective therapies,” said Matt Barnes, head of UK research & development, Sosei Heptares, in a company press release. “We provide a highly attractive approach that is recognized by many of the world’s leading biopharmaceutical companies and are delighted to add Lilly to that list. Lilly is a recognized world leader in diabetes and metabolic diseases, and we look forward to a collaboration that brings together our respective and complementary expertise with the goal of identifying and developing novel candidates to advance in these important areas of unmet need.”

News

Sosei Heptares and Eli Lilly have entered a multi-target collaboration and license agreement in diabetes and metabolic diseases. 

Sosei Heptares and Eli Lilly Enter Multi-target Collaboration

US-based Discovery Life Sciences (Discovery) has launched its new Proteomic Services Division, which will serve pharmaceutical and biotech companies, government organizations, and non-profit and academic research centers. The company’s innovative offering includes both exploratory and targeted proteomics using the Seer Proteograph Product Suite, SCIEX LC-MS/MS systems, Olink Proximity Extension Assay (PEA), and Luminex xMAP platforms, according to a Dec. 14, 2022 company press release.

Discovery’s proteomic service laboratories are now integrated with the company’s short-and long-read genomic service laboratories and global commercial biorepository of hyper-annotated biospecimens. Meanwhile, as a part of its biomarker services expansion strategy, the company signed a multi-year agreement in January 2022 with Seer, a US-based life science company commercializing a disruptive new platform for proteomics, and SCIEX, a life science analytical technologies company, to form the Proteogenomics Consortium (PGC), a first-of-its-kind, according to a company press release (1), for large-scale plasma proteomic studies. The PGC combines the proprietary technologies and expertise of all three companies. The aim of the consortium is to offer unbiased, high-resolution proteogenomic services to help uncover novel biomarkers that can be mapped back to underlying genetic variations.

“We are very excited to open our doors to researchers and scientists worldwide and provide them access to Discovery’s highly-scaled, multi-omic services to advance their biomarker discovery, clinical development, and large-scale research programs,” said Michael Pisano, Discovery’s executive vice-president of Proteomics, in the company press release. “Our proteomic experts carefully designed, built, and optimized a comprehensive, platform-agnostic suite of leading-edge proteomic and proteo-genomic technologies to support all biomarker strategies, large-scale population studies, and biobank characterization projects.”

“The Proteogenomics Consortium strategically unites these highly advanced plasma proteomic technologies with our … expertise in short-and long-read genomics to add deep, unbiased proteomics data more easily to discovery, translational, and clinical studies, as well as large-scale population research programs,” said Glenn Bilawsky, CEO of Discovery, in the press release. “In addition, the PGC also enables the creation of new diagnostic tools for cancer, neurodegenerative, inflammatory, infectious diseases, and other therapeutic areas.”

1. Discovery Life Sciences, “Seer, Discovery Life Sciences, and SCIEX Announce First-Of-Its-Kind Proteogenomics Consortium for Large-Scale Studies,” Press Release, Jan. 10, 2022.

The launch of Discovery Life Sciences’ new Proteomic Services Division offers a single source for large-scale population studies, biobank characterization, and multi-omic biomarker analysis.

Discovery Life Sciences Now Offers Large-Scale Proteomic Services

GSK and Wave Life Sciences, a US-based clinical-stage genetic medicines company, have entered into a strategic collaboration to advance oligonucleotide therapeutics focusing on novel genetic targets. Oligonucleotides are short strands of DNA or RNA that can reduce, restore, or modulate RNA through several different mechanisms. The collaboration includes Wave’s preclinical RNA editing program that targets alpha-1 antitrypsin deficiency (AATD), WVE-006.

According to a Dec. 13, 2022 company press release, the collaboration comprises two main components: a discovery collaboration and a late-phase development collaboration. In the discovery collaboration component, GSK can advance up to eight programs and Wave can advance up to three programs. In addition to these programs, GSK receives the exclusive global license for Wave’s preclinical program WVE-006. The discovery collaboration has an initial four-year research term and combines GSK’s insights from human genetics and its global development and commercial capabilities with Wave’s proprietary discovery and drug development platform, PRISM.

The second component of the collaboration involves bepirovirsen, an investigational antisense oligonucleotide for the potential treatment of chronic hepatitis B infection, and GSK4532990, a small interfering RNA (siRNA) oligonucleotide. Bepirovirsen is now entering Phase III trials, and GSK4532990 is moving toward Phase II trials for non-alcoholic steatohepatitis (NASH).

Under the agreement, Wave will receive an upfront payment of $170 million, including a cash payment of $120 million and a $50 million equity investment. Additionally, for the WVE-006 program, Wave is eligible to receive up to $225 million in development and launch milestone payments as well as up to $300 million in sales-related milestone payments. The company is also eligible to receive tiered sales royalties. After Wave completes the first-in-patient study, development and commercialization responsibilities will transfer to GSK.

Furthermore, for each of GSK’s eight collaboration programs, Wave will be eligible to receive up to $130 million–$175 million in development and launch milestones and $200 million in sales-related milestones as well as tiered sales royalties. Wave will be responsible for leading all preclinical research for GSK and Wave programs up to investigational new drug (IND)-enabling studies. Meanwhile, GSK collaboration programs will transfer to GSK for IND-enabling studies, clinical development, and commercialization.

“Oligonucleotide therapeutics are becoming a mainstream modality, and this collaboration will enable us to use our leading position in human genetics and genomics to advance novel oligonucleotide therapies. Pairing GSK's genetic expertise with the best-in-class PRISM platform enables us to accelerate drug discovery for newly-identified targets, by matching target to modality. The addition of WVE-006 complements more advanced, clinical-phase oligonucleotides in our pipeline, including bepirovirsen for chronic hepatitis B and GSK4532990 for [NASH],” said Tony Wood, president and chief scientific officer, GSK, in the press release.

“For the past decade, Wave has been building a unique oligonucleotide platform that combines novel chemistry with the means to optimally address disease biology through multiple therapeutic modalities. In 2022, we started to deliver on the promise of our platform with the first data showing translation in the clinic for our next-generation stereopure PN-chemistry containing candidates. Now, with our GSK collaboration, we are excited to leverage their expertise in genetics to continue building a differentiated oligonucleotide pipeline, with a focus on our … RNA editing and upregulation capability,” said Paul Bolno, president and CEO, Wave Life Sciences, in the release.

GSK and Wave Life Sciences have entered into a collaboration to drive drug discovery and development for novel genetic targets.

GSK and Wave Life Sciences to Collaborate on Oligonucleotide Therapeutics

PictorLabs, a Los Angeles-based digital pathology company developing an artificial intelligence (AI)-powered virtual staining platform, formally announced its launch at the start of December 2022. The company launched with more than $18.8 million in funding since its founding in 2019.

According to a Dec. 1, 2022 press release, the company’s technology is designed to apply deep learning algorithms to efficiently and accurately stain tissues 

, producing near-instantaneous virtual stains. From a single unstained tissue sample, PictorLabs’ proprietary platform can produce an unlimited number of virtual stains that are indistinguishable from analogous chemical stains. This eliminates the need for chemical staining, reduces the time to results, and allows for repeated biomarker testing without the need for multiple tissue samples.

PictorLabs has also demonstrated successful virtual staining for several well-validated disease biomarkers, according to the press release. The company is now pursuing additional opportunities in disease areas with high unmet need. The company plans to use its latest round of funding to expand its biomarker portfolio, develop novel products, grow the company’s talent base, and build out its corporate infrastructure.

“PictorLabs’ platform overcomes inherent issues in histopathology through the application of AI-powered algorithms that produce virtual stains which are indistinguishable from their chemistry-based counterparts,” said Yair Rivenson, CEO and chief technology officer, PictorLabs, in the press release. “By virtually staining tissue images in the digital realm, our approach simplifies and accelerates workflows, speeding up drug discovery and diagnostic decision-making with the goal of ultimately improving patient outcomes. With the support of our experienced investors, we’re now well placed to systematically expand our platform.”

PictorLabs, a digital pathology company developing an AI-powered virtual staining platform, formally announced its launch at the start of December.

Virtual Staining Start-Up Launches with More Than 18.8 Million in Funding

The US Pharmacopeia (USP) announced the opening of the USP Advanced Manufacturing Technology Lab in Richmond, Va., as a part of their launch of a suite of R&D analytical solutions to continue to support the efforts of drug manufacturers seeking to adopt advanced manufacturing technologies, such as pharmaceutical continuous manufacturing (PCM).

PCM is a continuous flow of materials through a process pipeline in which two or more unit operations are directly connected and there is a sequential transformation of the materials. The process output, such as an API or drug product, is collected as the input materials are being fed in, and the entire process happens in a single facility.

"Reducing technical and training barriers to adoption of advanced manufacturing technologies like PCM will help strengthen resilience by allowing medicines to be made in more places. This diversifies sources of supply and strengthens the medicines supply chain," said Ronald T. Piervincenzi, PhD, CEO of USP, in a press release. "Our new analytical laboratory service offerings extend USP's capability to assist manufacturers considering the adoption of PCM, and will help ensure availability of trusted, quality medicines made with PCM alongside those made with traditional batch manufacturing technology."

The R&D analytical solutions will consist of analytical lab services that use USP’s in-house scientific expertise and state-of-the-art facilities at the USP Advanced Manufacturing Technology Lab in Richmond and the USP headquarters facility in Rockville, Md.

The areas of focus that will be used to characterize materials and help ensure the quality of medicines made with PCM include:

Drug assays, including stability-indicating tests

Methods to control impurities (including mutagenic, process, and unknown impurities, as well as residual solvents)

Material isolation, identification, and characterization

Spectrometric and other process analytical technology applications.

USP is focused on R&D analytical solutions and quality measurements specific to PCM and is focused on addressing the challenges of PCM adoption and helping to strengthen medicines supply chain resilience.

In addition, USP is working with partners and stakeholders to address PCM knowledge gaps through educational programs, creating an online continuous manufacturing Knowledge Center, and launching a flow chemistry R&D lab to investigate novel routes of synthesis for APIs using PCM and develop new analytical techniques to ensure product quality.

PCM has the potential to allow flexibility and efficiency in manufacturing, lower production costs, cut environmental footprints, accelerate production and scale-up in response to emergencies, and reduce potential quality issues through real-time monitoring.

USP Advanced Manufacturing Technology Lab Opens for Further Developments in R&D Analytics

GSK, Sanofi, and Takeda announced a partnership with Singapore’s Agency for Science, Technology, and Research (A*STAR) and various Singapore universities on Dec. 6, 2022. The goal of this partnership, formalized through the Biologics Pharma Innovation Programme Singapore (BioPIPS), is to boost Singapore's biologics manufacturing capabilities, including recombinant therapeutic proteins and vaccines.

According to an agency press release, the partnership will bring together the companies and Singaporean researchers to enhance manufacturing productivity, improve operational efficiency, and reach sustainability goals. It will be composed of three workstreams:

The Sensing and Modelling workstream, focused on machine learning and mechanistic workflows to enable simplified and faster workflows,

The Sustainability workstream, focused on tackling sustainability challenges in biologics and vaccines manufacturing via exploration of novel materials, circular economy approaches, and models promoting more sustainable and resilient supply chains,

The Compliant Agility workstream, focused on removing manual tasks to improve productivity while retaining compliance.

“New opportunities will emerge as the biomanufacturing industry undergoes major changes brought about by the rapid pace of digitalization, Industry 4.0, and the need for greater sustainability,” said Lim Keng Hui, assistant chief executive, Science and Engineering Research Council, A*STAR, in the release. “As Singapore makes biopharma production a priority area in its Research, Innovation and Enterprise 2025 Plan, A*STAR aims to contribute our R&D capabilities through BioPIPS to help make the local biomanufacturing industry become more agile and better positioned to benefit from new products and technologies.”

“The objectives of BioPIPS are aligned to Takeda’s ambitions of being net carbon zero in our operations by 2035 and how we can tap on data, digital and technology to transform our manufacturing site. We look forward to this partnership with Singapore’s research ecosystem to further strengthen our capabilities and discover new and sustainable ways to develop and manufacture innovative medicine to deliver on Takeda’s commitment to Patient, People and Planet,” said George Lam, site head, Takeda Manufacturing Singapore, in the release.

The partnership will see the pharma giants partner with researchers from A*STAR and various Singapore universities.

GSK, Sanofi, and Takeda Partner With Singapore to Boost Biologics Innovation

Merck, known as MSD outside of the United States and Canada, and BigHat BioSciences, a biotechnology company specializing in machine learning (ML)-guided antibody discovery and development, announced a research collaboration on Nov. 29, 2022 in which Merck will use BigHat’s Milliner platform to design candidates for up to three drug discovery programs.

According to a company press release, the Milliner platform combines high-speed characterization with ML capabilities to engineer antibodies with more complex functions and better biophysical properties. The goal of this approach is to reduce the difficulty of optimizing antibodies and other therapeutics.

“We are excited to begin this collaboration to advance next-generation antibody therapeutics to patients,” said Elizabeth Schwarzbach, chief business officer, BigHat, in the press release. "This agreement with Merck brings us a major step closer to our goal of [three to five] deep collaborations with leading biopharma [companies] to complement our internal therapeutic pipeline."

“This agreement with BigHat expands Merck’s strategy of applying AI [artificial intelligence]/ML across our drug discovery capabilities,” said Juan Alvarez, vice-president of Biologics Discovery, Merck Research Laboratories, in the release. “We look forward to working with the team to leverage BigHat’s technology and expertise in enabling molecular design of novel biologic candidates."

Merck will use BigHat’s AI/machine learning-enabled platform to design candidates for up to three drug discovery programs.

Pharmaceutical Technology's In the Lab eNewsletter, January 2023