Pfizer is rapidly scaling up its AI/ML efforts in a collaborative effort intended to get transformative medicines to patients faster.
Pfizer’s Cambridge, Massachusetts site/courtesy of Pfizer
Artificial intelligence and machine learning (AI/ML) are key to enabling drug discovery and development, and Pfizer is leading the biopharma industry into the next wave of innovation. The company is rapidly scaling up and recruiting talent for a collaborative effort intended to get transformative medicines to patients faster.
The mandate is “uncompromising and extremely high-quality science,” Sandeep Menon, chief scientific officer, AI digital sciences, SVP and head of early clinical development told BioSpace.
The vision is three-fold: uncover disease biology with AI; use these insights to design the right molecules; determine the right patient population for clinical trial success.
“We’re building the next generation of tools to use across the preclinical and clinical development spectrum,” said Jared Christensen, vice president and head of early clinical development, clinical AI/ML and quantitative sciences.
Pfizer is building an “ML Research Hub” charged with creating novel predictive models and tools in what it called “a key investment.”
This team, headed by Enoch Huang, vice president, machine learning and computational sciences, will partner with experts across the company to ensure successful application of AI/ML by designing, deploying and maintaining state-of-the-art tools and techniques. It will uncover insights related to disease pathophysiology and generate relevant, testable hypotheses. The ML Research efforts will be led by Djork-Arné Clevert, who recently joined the company.
“AI/ML has been sewn into the fabric of drug discovery at Pfizer,” Huang said. “A sign of success is when our project teams or design chemists looking at compounds are using machine learning without knowing they’re using machine learning. That’s what’s happening behind the scenes.”
“However, we need to apply AI/ML beyond drug design, starting with the patient in mind,” Huang continued. “We see enormous potential to mine public and proprietary datasets using ML methods to better understand disease pathophysiology, which could potentially lead to breakthrough efficacy for patients that meaningfully change their lives.”
Numerous Therapeutic Applications
The innovation stemming from the collaboration will be therapeutically agnostic, Christensen shared.
“We’re going to start in areas where we already have a foothold,” he said. Pfizer’s core therapeutic focus areas are internal medicine, inflammation and immunology, oncology, vaccines and rare disease.
The lift will be lighter in oncology, where there have already been considerable advances in precision medicine. Pfizer plans to build on these gains to better understand patient populations and stratifications, Christensen noted.
“We are seeking out indications that are data-rich to train the models. The opportunity before us is to inform and impact target prioritization and patient stratification with AI/ML much like we have done in chemistry,” he said.
In internal medicine, Christensen highlighted heart failure, diabetes and non-alcoholic steatohepatitis where there are large populations and more data accumulating each day. The same can be said for inflammatory and immunological diseases such as rheumatoid arthritis, Crohn’s disease and ulcerative colitis.
Pfizer intends to harness this data, together with relevant biomarker and next-generation sequencing datasets, to better understand where its drugs can have the most impact.
“I’m a firm believer that diseases we now call one thing will continue to subdivide based on biomarkers and clinical phenotypes,” Christensen said. “I believe that kind of revolution is going to continue to come to other diseases, similar to what we have seen in oncology. We’re trying to catch and ride that wave.”
It might not be too long, either, before the wave crests.
These and other clinical use cases will help drive methodology development within the ML Research Hub. Subha Madhavan was recently recruited as head of clinical AI/ML and data sciences within early clinical development to help define core requirements from drug programs that will tap into the innovative methods from the Hub to accelerate development.
These efforts will utilize historic clinical trial data, biomarker data and real-world evidence such as from electronic medical records to precisely define patient populations to inform study design.
It’s ultimately about improving the probability of technical and regulatory success of Pfizer’s clinical trials, Madhavan said.
“Within clinical AI/ML, we’re really driving a paradigm shift in precision medicine. Our focus is on using multimodal data to inform trial design, first-in-human studies [and] our sign of clinical activity studies.”
Pfizer is applying advanced methods like classical and deep learning to molecular data sets collated from its own clinical trials and published studies “to identify the patient subpopulations that might better respond to a certain treatment,” she explained.
“I’m very optimistic about our ability to tap into multimodal, high dimensional datasets and also rapidly develop algorithms to predict a variety of outcomes for patients.” She predicts that the impact of many of these new innovative tools will reach patients within the next three-to-five years.
Madhavan was attracted to Pfizer by the company’s “lightspeed thinking” and “cross-functional” approach to drug development.
“Pfizer is a company that, even though it’s one of the ‘big pharmas’, can pivot very quickly, as demonstrated by the COVID vaccine and anti-viral programs in response to the global pandemic,” she said. “The culture has transformed into one where we can take advantage of these [cross-functional] teams and bring innovation to multiple therapy areas.”
“We are taking a disciplined product development approach to define business value, key stakeholders, core functionality and usage for each AI/ML model to help align with and accelerate our portfolio,” she added.
Applying Cutting-Edge AI/ML
Pfizer is also applying AI/ML to digital medicine in the Pfizer Innovation Research (PfIRe Lab). Here, researchers are developing algorithms for wearable devices to help scientists monitor symptoms, assess health and better understand how treatments work.
Wearable devices provide researchers and physicians with “a complete and continuous picture of the patient’s experience” during the assessment period, Menon said, rather than relying on the patient’s memory at a single office visit.
Cutting-edge advances abound in AI/ML, but Pfizer is particularly interested in the ones that can help it reach patients with leading-edge medicines. As Menon said, “we are not using AI as just a fancy term or shining object. It is all about tangible and executable solutions to key research questions.”
Christensen highlighted explainable AI as an area that can help build science around disease.
“We’re looking for new computational models that are less black box and more open to understanding what’s going on underneath the hood,” he said.
When it comes to understanding the molecular basis of disease pathophysiology, Huang pointed to a powerful ML architecture called Transformer, which was developed with language models in mind. Transformer is the basis for Google Translate.
“It can help us to understand the biomedical literature through natural language processing, which is an area of great interest to us at Pfizer,” he said.
Madhavan said knowledge graphs can help connect genes to diseases and drugs and help identify novel biomarkers associated with certain disease pathways. Knowledge graphs can also draw connections between patient phenotypes and enable researchers to develop more effective treatments for these patient groups.
Bilingual Data Scientists Wanted
As Pfizer builds out its AI/ML-focused teams, the company is “rapidly recruiting” data scientists.
A dynamic mission requires a dynamic mindset that can sort through complex data to make the correct scientific and therapeutic connections.
“There are a lot of data analysts out there that have incredible skill, but we need to marry that skill up with people that understand the science and are willing to take a scientific lens to these hypotheses,” Christensen said. And with a strategic vision that spreads across so many divisions and specialties, “a communicative and collaborative mindset,” is another key attribute.
Madhavan noted Pfizer is looking for bilingual data scientists with a deep understanding of both data science and clinical science. The desired candidate will have “work experience where they have actually deployed their quantitative skills to answer key clinical and/or biological questions,” she said.
In the ML Research Hub, Huang is also looking for ambidextrous scientists who not only have ML research and data engineering expertise but also understand chemistry and molecular/cellular biology.
As every scientist knows, the key to any experiment is reproducibility. “If it is not happening in the real world, I think you’re just going to do more damage than good,” Menon said.
He emphasized the importance of responsible AI. “A lot of times, AI is used as a buzzword. It is basic statistical modeling and mathematical modeling but if that is not done by subject matter experts who are experts in the science, it’s going to be a weapon that will backfire.”
Christensen laid out the opportunity for potential team members: “It’s early days, but it is a great opportunity for data scientists who want to build foundational and lasting systems to help us make data-driven decisions to innovate across the entire drug development paradigm.”
Those interested in joining the Pfizer team can find more information here.
