DDW’s Megan Thomas speaks to decision-makers from the drug discovery industry about their predictions on what 2022 holds for our sector.
Around this time last year, the first Covid-19 vaccines were authorised for emergency use by organisations such as the FDA1, the EMA2, the MHRA3 and many more internationally. A year before that, Covid-19 had not yet been declared a pandemic4. It goes to show how much can change in this industry, as well as how important this sector is. With what we know – having adapted constantly since March 2020 and having also experienced uncertainty as new variants are discovered – predictions from industry experts for 2022 are important and illuminating.
Anita Cooper is a Non-Executive Director on RareCan’s board. With over 30 years’ global business experience, Cooper has worked across diverse functions, skill levels and geographies, including strategy development and implementation, risk management, operations and technology-enabled process transformation internationally.
Looking ahead to 2022, Cooper says: “There is an opportunity to shift to a more efficient outcomes-based, patient-centric drug development and delivery model. Further stakeholder engagement in clinical trial design and healthcare delivery will be necessary before this opportunity can be realised; a truth that is especially relevant for rare cancers.
“Enabling technologies will be needed to underpin this shift and the biopharmaceutical industry should be encouraged to embrace this transition towards a more holistic and personalised, patient-centric culture. Looking at therapeutic areas specifically, in oncology the rate of the development of personalised medicine will continue to accelerate with continued segmentation of patient populations based on predictive biomarkers.”
As well as these opportunities, there are also challenges to consider. “Clinical trials continue to become more complex. Alongside this, large and growing quantities of patient data are being made available for researchers’ use. This is creating a significant challenge for researchers when collecting, curating, and analysing large datasets to produce meaningful information. The disjointed nature of many of the resources available to pull patient data from, continues to provide a challenge. In many disease areas, particularly in oncology and rare disease, patients’ needs should be considered in their broader health and wellbeing context. Doing so gives further value to clinical trial data, and more insights into future drug design and development,” says Cooper.
Speaking about the impact of the pandemic on the future, Cooper says: “The pandemic has revolutionised many aspects of the drug discovery and development process. Risk-based clinical trial monitoring, underpinned with integrated data analytics and a confluence of on-site, hybrid and virtual trial management, will inevitably continue to provide powerful tools for drug development.”
According to Cooper the shift in industry culture towards promoting a more patient-centric model of work and adapting to social distancing measures during the pandemic has led to a greater provision of home-based clinical trial assessments. “The rise in the use of wearable reporting devices and of ePRO (electronic Patient Recorded Outcomes), eConsent, eCOA (electronic Clinical Outcome Assessments) and telemedicine throughout the pandemic has facilitated this transition towards more remote health monitoring. As more tools are developed in this space, a greater diversity of patients will be accessible to researchers as geographical and other barriers to participation, are lowered. This may have a much needed and positive outcome, in researchers having a broader base of patients from which to recruit into studies and ultimately facilitate greater patient access to medicines,” she says.
“There is also increased awareness of the importance of clinical trials in the drug development process due to the scrutiny that the sector has faced during the research and production of Covid-19 vaccines. This will have a number of knock-on effects in the industry, but should specifically heighten interest in and investment into developing more efficient processes for drug discovery and development, regulatory advice and approvals, post-licencing drug management and patient access to drugs in clinical trials and licenced medicines.”
Talking about the lessons companies can learn from the pandemic for the future opportunities and challenges in the sector, Cooper says that one is the power to innovate through collaboration and partnership across academia and the biopharmaceutical industry. “The importance of working in close consultation with regulators, globally cannot be under-estimated in the development and licencing of new vaccines and medicines and in the access for patients, to those products,” she says.
According to Cooper investment in the technology that underpins the drug research and development process has helped the industry adapt to new circumstances.
“Finally, efficient supply-chain management globally, throughout the drug development and delivery life cycle will have huge benefits across the industry. Having platforms available to manage vast amounts of data in this field will have a pivotal role to play. Improved data and resource management will streamline the drug development process and enable life-saving treatments to hit the market faster.”
Mateusz Kaczyński, Director at Ellarion Cybernetics, specialises in obtaining and processing data in a robust, scalable and consistent way. “The dynamic shift towards data-driven methods and approaches is slowly reaching the tipping point. We see biotech and pharma companies increase computational / data-driven strategies, and early adopters already benefit from their investments. For us, this means we need to deliver critical proof points to demonstrate the applicability of our technology in this changing landscape,” Kaczyński says.
He observes that the industry has slowly started to apply more ideas taken from the technology sector. He predicts faster prototyping, parallel experimentation and thinking in terms of maximising long-term learning. He says: “Adopting ideas from the tech giants, even by following them, may help break the long, low yield, linear project cycles, with clinical trials ripe for the transformation.”
With these challenges and opportunities in mind, Kaczyński thinks that to fully realise the potential of the Cambrian revolution of computation and technological approaches, companies need to evaluate the landscape and what, if any, methods may benefit their particular use case. He states: “The next step is prototyping the solution once they identify a tangible, practical opportunity. This may include attracting and training the best available talent at the intersection of drug discovery and technology, either via targeted recruitment campaigns or direct acquisitions. Also, realising the value of experimental data companies already have and shaping it to be reused or sold.”
“AI seems to slowly permeate on all levels of the drug discovery process,” Kaczyński says. “From biologics and target identification, through chemistry simulation and optimisation to clinical trial recruitment and delivery. For Ellarion Cybernetics, it is about finding the best fit for our biomedical data acquisition and management engine. We are slowly reaching the stage where the phrase ‘using AI / machine learning’ will be as redundant as ‘using statistics’.”
Eric Wolford, Vice President of Global Medical at Bio Products Laboratory, has managed and led clinical trial and medical affairs work in the pharmaceutical and biotech space for more than 25 years. He says 2022 discovery challenges will be shaped by the Covid-19 pandemic and that companies have shifted resources away from other projects to provide pandemic solutions. According to Wolford, the availability of subjects willing to particpate in clinical trials has suffered, broad scientific skepticism has adversely effected recruitment, and how supplies of reagents and other materials for pre-clinical discovery are in short supply due to supply chain issues, with no immediate relief in sight. Adding to this, he says small patient populations in rare diseases and trials with difficult endpoints will remain problematic and that in larger companies, unmet clinical need in ultra-rare patient populations could be deprioritised in favour of larger projects.
Sharing his predictions on the sector’s opportunities, he says: “Technology associated with mRNA vaccines has gained significant press, and will likely be utilised in oncology, immunotherapy, and other disease areas. Monoclonal antibodies have become a hot topic due to the pandemic. In addition, a cottage industry of companies, technologies, expertise, etc. has developed to address future pandemics, and will spill over into other areas.”
To best prepare for what 2022 holds, Wolford says companies will need to secure additional resources and expertise. “Staying abreast of developing science and carefully distinguishing true opportunities from passing fads will be key”, he explains. “Collaborations and other partnerships can help provide much needed resourcing and expertise. Academic collaborations and government funding sources may be more important in the near-term.”
Raj Kapadia, Vice President of Global Sales, Marketing and Strategy at Integrated DNA Technologies (IDT), has nearly 15 years of experience in the life sciences and diagnostics space.
Discussing the challenges IDT will face in 2022, as well as the wider drug discovery sector at large, Kapadia says: “As with many industries, supply challenges are expected to continue into 2022. Certain sectors, like drug discovery and broader clinical research, will further be exacerbated by the renaissance enabled through new tools that will transform genomic medicine. The continued innovation of modalities also will pose challenges when planning for future demand.”
Kapadia thinks genomic medicine offers significant opportunity. “IDT is rising to this challenge by continuing to innovate on its CRISPR research offerings and invest in future needs by scaling its manufacturing with cGMP API compliant processes (Q7) to support customers through the drug discovery and development process,” he says.
Importance of collaboration
According to Kapadia, novel genetic medicine modalities and existing biologics will require new views on traditional supplier, CRO, CMO and pharma/biotech models. “To get ahead, businesses that drive more collaborative partnerships with technology and service providers will gain a competitive advantage. Partnering as early as possible will help reduce time wasted from bridging studies throughout the drug discovery and development process,” he states.
Looking at the role of AI in the future of the industry, he says: “The journey to machine learning is multi-staged. It begins with driving connectivity between key systems and products installed along the drug discovery process to enhance target identification and validation. Next, informatics tools that help capture and train key algorithms will be needed to increase efficiency and performance. The final stage will result in AI-driven systems that generate genome modification designs optimising biologic utility of novel cell therapies.”
Dr Richard O’Kennedy is Vice-President for Research, Development and Innovation at Qatar Foundation.
Noting how the Covid-19 pandemic has triggered international collaboration in health research and drug development on a level never before encountered, Dr O’Kennedy says: “This is highly promising both for the future of anti-virals and also more widely in healthcare, as we look to identify suitable therapeutics for other diseases and conditions. Since health threats and diseases transcend borders, significant opportunities are available if we are highly collaborative and globally focused in our approach to tackling these issues.”
Dr O’Kennedy thinks 2022 will be an exciting year for precision medicine. He says: “Many countries have been making rapid progress on mapping genomes, and the pandemic served to accelerate international collaboration. In 2020, the Covid-19 Host Genetics Initiative began, with more than 3,500 researchers from 25 countries collaborating on one of the largest genome-wide association studies ever performed to find a solution to the pandemic crisis. Its results have proven the value of diverse datasets and how, with access to the right data, existing drug therapies can be matched to health issues at an improved time and cost-efficient rate.”
Open data sharing
Looking beyond the immediate crisis, Dr O’Kennedy says that to successfully introduce precision medicine at scale, researchers, drug developers and practitioners need to maintain this collaborative approach. “Historically, health research has often been largely siloed. Yet, open data sharing means we can make precision medicine much more inclusive, ultimately representing a complete range of ethnicities, genders and ages in research and identifying therapies that are highly tailored to individual needs so that everyone benefits.
According to O’Kennedy, technology, specifically AI, will provide vital support as we extend our datasets, increasing the efficiency of analysis by enhancing the speed and accuracy of data interpretation. “When correctly applied, AI can provide valuable analyses from these global datasets at a rate far beyond human capability, and as a result we should start to see major advancements in drug design, selection and discovery at a much quicker rate than otherwise achievable. Such approaches can provide particularly effective insights for effective drug repurposing, enabling established drugs to be identified methodically for clinical trials for new disease targets, rather than requiring researchers to do this by trial and error, when they are essentially looking for a needle in the haystack. This also has significant economic benefits for drug discovery,” he says.
“While AI holds great promise, it is not without challenges. There are concerns about ethics and privacy, with many questions still unanswered about how we can guarantee anonymity and protect individuals’ personal health information. These challenges and concerns must and can be addressed as they are a priority to increase public trust in such technologies, so that we can fully leverage AI’s incredible potential and extensive capabilities and roll out precision medicine at scale.”
Dan O’Connor, VP of Drug Discovery at Molecular Devices, is an expert on innovations in drug discovery like organs-on-chips, organoids, and automation. With regards to the challenges Molecular Devices will face in 2022, as well as the wider drug discovery industry, he says: “There’s so much opportunity to improve drug discovery, mainly by finding ways to improve generation and screening of more physiologically-relevant disease models at scale. While innovations have made drug discovery tools faster and more sensitive, nine out of ten drugs tested on the industry-standard, immortalized cell lines, still do not translate to reliable responses in patients, wasting time and money. Our mission at Molecular Devices is to equip researchers with the tools needed to bring breakthrough drugs and therapies to market sooner.”
With challenges come opportunities, and O’Connor believes that the most successful approaches to improving clinical outcomes in drug discovery will combine AI, simulation, and traditional experimentation using 3D models and cell engineering techniques. He says: “Molecular Devices intends to leverage its expertise in Cell Line Development, BioImaging and innovation to empower its technology to perform drug discovery workflows with benefits that are not available today. A lab of the future will include intelligent, autonomous decision-making platforms powered by AI and seamlessly incorporate stem cell engineering (CRISPR) with long-term 3D cell culture experiments that span over months with QC checks along the way. One last key element is the ability to monitor and operate remotely from around the world.”
In order to prepare for these challenges and opportunities, O’Connor says that academic, government and industry collaboration will be critical as we transition to more complex workflows utilising 3D models. He says: “I think the most successful technology companies will have scientific experts themselves that can help guide the researcher.”
Using AI to track cells
O’Connor explains that by using data-driven decision making, AI/ML has the potential to both speed up the discovery process and reduce failure rates. He said: “Specifically, if we think about how facial recognition technology is used today, in the future, AI will be tracking every cell by its own unique ‘fingerprint’. The entire cell’s journey from single cell isolation, gene editing, cell line expansion, differentiation, organoid formation, and finally functional screening, would be recorded and audited. While this will provide more clarity and confidence in drug efficacy, it also provides a step-by-step manual proving authenticity of discovery. I am convinced this approach will be the gold standard for drug discovery in the future, as regulatory agencies such as the FDA continue to require more stringent reporting of the process to develop a therapeutic for patients.”
Josh Gluck, Vice President Global Healthcare Technology Strategy at Pure Storage & Adjunct Professor of Health Policy & Management at New York University Robert F. Wagner Graduate School of Public Service, shares what he considers to be the most important trends to look out for in 2022.
The first trend he identifies is that a “voracious appetite for faster-time-to-science is here to stay”. He says: “The world’s scientific community continues to break records in the fight against Covid-19 – leveraging massive information sharing that is leading to a more accurate picture of Covid-19 and accelerated development and testing of vaccines and therapeutic treatment candidates. Health sciences organisations across the board seek to build on this momentum safely and effectively to further accelerate the pace of personalised medicine. Genomics and artificial intelligence (AI) are key to this quest. To realise AI at scale, however, requires liquid data and modern data infrastructure that re-imagines the role of data and how it is used.”
Dr Frank Craig, CEO of Sphere Fluidics, has 20 years of international, biotech and product development experience, including at GSK, Amersham Biosciences and several start-ups. Thinking about 2022 and the challenges ahead for Sphere Fluidics, he says: We have just raised $40 million in investment from leading European and US VC funds and will have the challenge of now carefully using these funds to support international growth in our sales and service and support and also enable rapid development of new products and applications for our Cyto-Mine instrument.”
Looking beyond Sphere Fluidics to the drug discovery sector in 2022, Craig says: “The drug discovery sector has been hit by Covid impacting its output (eg. Covid shutting down labs and research activity) and also availability of some materials and products (due to supply chain issues). I believe that this impact will still be evident, but perhaps not as strong, in 2022.”
Opportunity from new technology
Despite uncertainty around Covid-19, there are still opportunities in the year ahead. “The biggest opportunities are in the generation of new biopharmaceutical molecules created by new technologies, such as bispecifics, and in new cell therapies created by various genetic and cellular techniques. One can also see the ‘rise of AI’ being impactful as more powerful computers and informatics enable data to be used (and re-used) in different ways to identify the causation of disease, new drug targets and even new therapeutics,” he says.
In order to be prepared for these challenges and opportunities, Craig says that drug discovery and development businesses can do several things: Raising enough investment for their needs; ensuring that they have the right skill sets, people, and technologies; and aligning their activities with markets that have the best potential. He says: “Fortunately for life sciences, Covid doesn’t seem to have impacted VC investment activity so, with the ‘right story’, currently there appears to be ample funds available.”
David Hughes, CEO of CN Bio, believes 2022 will be a year with great growth potential for CN Bio. However, that is not without challenges. He says: “The many uncertainties related to the Covid-19 pandemic, for example new waves of infection, changing working patterns and supply chain issues could make this another challenging year. I feel the same is generally true across the drug discovery sector.”
There remains opportunities, despite this uncertainty. He states: “When faced with the emerging threat of Covid-19, the pharmaceutical industry responded with rapid vaccine development – the likes of which has never been seen before, repurposing of existing therapies and development of new medicines. This is a testament to the speed at which innovation can occur if the problem is sufficiently pressing. The pandemic demonstrated that years of antiviral research could be mined and exploited in mere months. Can the same thinking and sense of urgency, now be applied to other diseases?”
Looking at both these challenges and opportunities, Hughes says: “I believe there is a great opportunity to improve speed and efficiency in drug discovery, all the more important given the time lost to the pandemic. Companies should critically question current methodologies: are they providing the best possible information, in a timely fashion to facilitate project decisions? New data sources and technologies are now offering deeper insight into biology and disease, including more human relevant test systems in which to evaluate candidate drugs. Investing now to make these a part of drug discovery processes will drive future productivity.”
Hughes sees significant potential for AI in the drug discovery industry. He says: “AI and machine learning are fantastic tools for efficiently hunting the chemical and biological knowledge space to find targets, initiate programmes or refine chemistry – they will drive a new wave of drug discovery. For that wave to be efficiently converted into medicines, a new generation of in vitro and in vivo models is required, which are more predictive of human responses to allow for more rational selection of compounds and programmes. Consequently, CN Bio sees a complimentary relationship between our predictive Organ-on-a-Chip assays and AI-driven drug discovery.”
According to Neil Torbett, Chief Operating Officer of PhoreMost, as the company heads into 2022, its biggest challenge is to advance its growing pipeline. “The company is well placed to meet these challenges, having recently scaled our discovery team and grown our facilities, following a £33M series B investment in March. Additionally, PhoreMost also has an array of collaborative drug discovery partnerships which extend our reach enormously,” he adds.
Drug target opportunties
Torbett thinks there are several large opportunities for drug discovery in 2022. He says: “New technologies relating to drug target (and site) identification are creating many opportunities for the drug discovery sector. For example, advances in protein structure prediction (for example AlphaFold) together with machine learning-based drug discovery are set up to massively open up new druggable space.
“The second area of opportunity we see is the new and emerging modality of Targeted Protein Degradation (TPD) drug discovery. The concept that drug targets can be ‘destroyed’ rather than modulated by conventional inhibitors is a major industry focus.”
Torbett feels that businesses can prepare for these opportunities by investing in future technologies such as AI and machine learning. He adds: From our perspective making synergistic biology-focussed investments and/or engaging in multi-disciplinary collaboration that can truly unlock the power of next generation technologies will be crucial.
“PhoreMost is already incorporating AI and machine learning across its entire discovery pipeline. The company is well placed to bridge the gap between the old worlds of traditional disease biology understanding and drug discovery and the brave new world where next-generation drugs will be computer generated.”
Kashef Qaadri, Software Technology Leader at Bio-Rad, thinks that generally, one of the biggest challenges to be faced in 2022 is the “cumbersome” drug development process combined with a “lengthy and often difficult drug approval process forces researchers to be innovative”. He says: “Particularly in the context of Covid-19, our industry has been forced to re-think how we discover and develop drugs. I would imagine this trend of working remotely and reducing the time spent on-site in the lab will continue into 2022 and beyond. From a technology standpoint, this will usher in increased automation/robotics and decreased manual ‘hands-on’ time. This should also lead to more automated documentation and traceability. Additionally, as organisations shift towards remote or hybrid models, IT groups will need to re-evaluate measures and processes to ensure connectivity, while maintaining security and compliance. Within our organisation, we’ve been forced to rethink what tasks need to take place within an office, lab, or remotely.”
Predicting opportunities in 2022, Qaadri says: “In an effort to decrease cost, maximise throughput, and increase reproducibility laboratories are looking at lab automation, commonly termed ‘Lab of the Future’ (LoTF). These efforts are transforming the lab, enabling scientists to develop the next generation of drugs and therapeutics with speed and efficiency. In addition to streamlined design of experiments, automated data acquisition from the instrument, remote access/monitoring and automated/reproducible analysis are large opportunities.”
Qaadri thinks that the drug discovery and development organisations are technology averse. He said: “There has been a steady shift towards the cloud, for instance, but adoption is at a much slower pace compared to other industries, such as finance and media/entertainment. As scientists and as organisations, we must embrace new technologies in order to help accelerate research.”
Talking about AI at Bio-Rad, Qaadri states: “We are exploring how AI tools can be leveraged to accelerate research and development, process development, manufacturing, and clinical trials. Providing customers access to their data is the first step, and we’re starting to make that possible with the launch of our BR.io platform.”
Dr Chris Lowe, Head of Research Operations at Horizon Discovery, is looking at the opportunities ahead. He says: “After the successes of the recent RNA vaccines, I expect there to be a surge of interest in drugging RNA in 2022 and beyond.”
To prepare for these challenges and opportunities, Dr Lowe thinks a clear purpose for an organisation is key to attract the talent with the right skills required to deliver the company’s goals. He adds: “Additionally, make sure your supply chain is robust and being actively managed as disruption looks set to continue to cause problems through 2022.”
AI and ML
On the topic of AI/ML in drug discovery, Dr Lowe is sure its pivotal role will continue. He says: “As the ability to delve deeper into biology via single cell technologies continues to expand, the datasets that are created increase to a point that searching for the valuable information exceeds a person’s capabilities. AI/ML should mean unbiased assessment of that data creates novel insights, accepting that the models are generally only as good as the training data used to develop them.
“This isn’t just limited to drug discovery. As labs become more automated, process improvements will also be driven by the operational metrics that are captured routinely.”
Sean McClain, Founder and CEO of Absci, says that the drug discovery sector may see challenges with waning of the Covid-19 tailwinds that propelled enthusiasm for the industry in 2020-2021.
That said, McClain identifies opportunities, too: “The majority of approved and clinical stage therapies are designed to intervene with one of a handful of known targets, and there is enormous biotherapeutic whitespace for novel target identification. Identifying novel targets is both a challenge and a tremendous opportunity for advancement in the industry.”
He says that to prepare for this, “the best way to prepare is to deeply integrate AI methods with bench science to enable not only hypothetical model development, but real-world testing and iteration. Companies that invest in processes for data generation and curation tailored to AI applications may emerge with competitive advantages.”
Looking deeper into the role of AI and its future in the sector, McClain says: “Absci is merging AI with synthetic biology, training deep learning models with data we generate on protein function and cell line design. Our vision is to enable fully in silico drug design for any given target; AI is central to our current efforts and future endeavors.”
Alberto Pascual is a doctor in bioinformatics with experience in data science and biomedical domains. Currently he is the Director of Data Science and Analytics at IDBS where he is responsible for developing and executing IDBS’ strategy on AI, data science and analytics to deliver products and services to customers in life sciences R&D markets.
Looking at the current state of data use and looking at what 2022 holds, he says: “Making sense of large volumes of data once you have it is no simple feat, particularly if your company isn’t fully digital. The reality is that a large proportion of companies still primarily use Excel or even paper to record data—and even if they’re partially digitized, data are often kept in data silos, lakes, or warehouses that are not fully integrated. Having the information and data properly organized and contextualized will boost the creation of new analytics, ML and AI application to automate the extraction of knowledge. 2022 will see faster adoption to digitisation and those who adopt a digital-first strategy will see significant gains.”
Adapting to face challenges
Pascual believes we need to shift away from the old systems of data storage and management toward one that curates data and provides integration and contextualisation in a way that’s easy to understand. He explains, “Adoption of this type of system— like a biopharmaceutical lifecycle management system, or BPLM—not only enriches data collection and analysis, but it makes possible the creation of digital twins in biopharma.”
With regards to preparing for what’s to come, Pascual says: “The key is that a BPLM creates a comprehensive data backbone across the entire development lifecycle—the data is contextualized, as it is collected at the point of generation, across multiple stages and processes. One way that we can get there is by collecting and collating data across conditions. Disturbing cellular environments in a multitude of ways, or changing conditions in the processes, instruments and workflows and measuring the outcomes, can build a vast mountain of data that allows you to begin to create accurate models.
AI and ML for IDBS
Pascual says: “Again, an important benefit of a BPLM system is that it makes the digital twins possible. Captured data helps in “training” digital twins and make them valuable predictive and even troubleshooting tools. As we develop ever more sophisticated therapies and technologies, the digital twin will be even more valuable. mRNA vaccine development, for example, is particularly well-suited to the process, due to the complexity of the many phases involved; a digital replica can help optimize the developments. One clear example is that scientists can tweak the mRNA code in silico as new variants come up, to create an updated version of the vaccine to serve as a booster.
“In essence, a BPLM sits at the center of our movement into the future and into biopharma 4.0. By collecting and analyzing data in a totally new way, the BPLM is a “hub” that facilitates integration across systems with the internet of things and commoditising AI and advanced analytics. Partners working together will be needed to fully realise this integration—first across lab instruments, then labs and facilities, and then one day across disciplines.”