In this blog, we explore the immense potential value that large language models (LLMs) can bring to life sciences, focusing on areas such as disease biology, literature analysis, genomic data analysis, knowledge graphs, clinical trial design, clinical trial engagement, drug manufacturing, precision marketing, and medical affairs. Further, we discuss the path to harnessing this potential, considering the limitations and risks associated with proprietary data and the use of public models, which can be particularly important in this field.
1. Unlock Disease Biology Insights
One of the exciting possibilities offered by LLMs is their ability to extract scientific relationships and disambiguate scientific entities currently stored in the vast amount of unstructured text of scientific articles within PubMed and other repositories. Currently, researchers are required to search for and read thousands of publications, before eventually manually distilling this information and formulating new insights. By employing LLMs, researchers can rapidly delve into vast amounts of scientific literature and extract the precise information and insight they are searching for. As newer models such as GPT4 come on line that process both text and images, this capability expands to include intricate details from tables, graphs, and other sources. This not only greatly accelerates existing discovery processes but further enables a deeper understanding of disease biology, potentially uncovering new insights and accelerating biomedical research.
2. Discover Biomarkers from Genomic Data
LLMs demonstrate great potential in the field of bioinformatics and genomics. Genomic data analysis is a complex task in the life sciences field, with current approaches barely scratching the surface of translating the codes of life. LLMs will have the ability to process raw genomic data to enable researchers to uncover patterns, identify biomarkers, and gain a holistic understanding of genetic factors underlying various diseases. The LLM’s proficiency in handling large datasets can expedite the discovery process and enhance precision in genomic analysis, interpreting genetic variations, and predicting functional elements within the genome. By leveraging their language processing abilities, these models aid in uncovering meaningful insights from genomics data, facilitating the discovery of disease-associated genes, and advancing our understanding of complex genetic mechanisms.
3. Design Preclinical Assays
By analyzing scientific literature, LLMs can aid in the identification of suitable methods, cell lines, and animal models to develop pre-clinical assays. Proper experimental design and reagent selection is an essential step in discovery, often bordering on an art as much as a science. This process is also fairly tedious and requires significant research in areas such as assay creation, plasmid design, knockout study design, or identifying the optimal conditions for cell culture. LLMs ease this burden by quickly scanning and summarizing vast volumes of scientific literature. Researchers can leverage the capabilities of LLMs to identify key findings, extract relevant information, and generate concise summaries. This expedites the literature review process, enhances knowledge dissemination, and aids in hypothesis generation for further scientific investigation.
4. Transform R&D Knowledge Management
R&D teams produce a significant amount of heterogeneous data and data types. This data includes pre-clinical assay results, key findings and recommendations, industry analyses, publications, conference proceedings, clinical trial data, real-world data, market intelligence, and strategies for partnerships, licensing, and M&A. Ideally, all of this information is analyzed holistically to make the best informed decisions on future discovery. However, given the breadth and volume of this data, much of this process tends to be siloed with fragmented decision making. Knowledge graphs enable life science organizations to integrate vast sets of heterogeneous and unstructured data. Knowledge graphs can unify proprietary organizational data and public data. Deploying an LLM specifically focused on a proprietary knowledge graph enables valuable and proprietary insights to support the memorialization of institutional knowledge and more data-driven decisions.
5. Enhance Clinical Trial Protocol Design
Clinical trial protocol design is a critical aspect of drug development, and the application of LLMs can significantly improve this process. Incorporating the model's insights across a large set of past clinical trial protocols can reduce amendments and create cleaner protocol designs in the future. Further, the use of an LLM as a chatbot to answer questions by the study team in real time about the protocol will encourage compliance and help identify protocol considerations that create confusion and reduce efficiency. By including the success, failure, or challenges of past studies, this approach could identify problems in a clinical trial even before it starts, allowing for early intervention and optimization. The use of LLMs in clinical trial protocol design and management can optimize trial design and execution, potentially leading to more successful and efficient clinical studies coupled with better patient care.
6. Improve Clinical Trial Engagement
LLMs could be used to create more frequent, more empathetic and more personalized communications with clinical trial participants. Participants who feel appreciated and who understand the importance of their contributions to medical research are more likely to remain engaged in a clinical trial. Research has shown that these communication strategies are effective in reducing clinical trial attrition, with a significant positive impact on trial timelines, cost, and success. LLMs are already being used to optimize patient care and clinician interactions with their patients thereby paving the way towards inclusion within clinical trials as well.
7. Streamline the CDMO Handover Process
Increasingly, biotech and pharmaceutical companies are relying on Contract Development and Manufacturing Organizations (CDMOs) to develop and manufacture increasingly more complex therapeutics. A high quality handover process for complex manufacturing instructions of sophisticated therapeutics between biotech and pharmaceutical companies and CDMOs is critical for successful drug development. This process is currently extremely manual and fraught with delays and challenges. LLMs can improve this process by automating the extraction and organization of crucial information from relevant documents and structuring them into the format expected by the CDMO. The LLM can also flag areas of concern or ambiguity, identifying information that hasn’t been encountered in prior manufacturing projects. This approach would not only reduce manual effort but also improve the accuracy and efficiency of the handover process, improving the quality of the subsequent manufacturing process.
8. Empower Commercialization and Precision Marketing
With so many precision therapeutics in clinical trials increasingly targeting smaller patient cohorts based on genomic and other biomarkers, there is an increasing need to be able to identify and communicate with targeted patient populations. Precision marketing in the life sciences industry involves targeting the right audience with personalized messaging and content to drive engagement, brand awareness, and ultimately, improve patient outcomes. LLMs can interpret vast amounts of unstructured data from social media, claims, and other real world data sets to identify providers who have patients that may benefit from targeted therapies. Life sciences companies can gain insights into customer preferences, behaviors, and needs, enabling them to segment their target audience more effectively. This enables the delivery of personalized marketing messages to educate potential patients on the benefits of therapy, tailored to the specific interests and requirements of different customer segments. By leveraging LLMs, life sciences companies can optimize their marketing efforts and create meaningful connections with patients and other stakeholders.
9. Transform Medical Affairs
LLMs offer transformative opportunities for Medical Affairs to enhance healthcare engagement, scientific communication, and evidence generation. By leveraging LLM capabilities in scientific education, medical information dissemination, key opinion leader (KOL) engagement, and real world evidence (RWE) generation, Medical Affairs teams can streamline processes, and improve access to information. One of the key responsibilities of Medical Affairs is to provide accurate and up-to-date scientific information to healthcare providers (HCPs), researchers, and other stakeholders. LLMs can assist in the creation of educational materials, summarizing complex scientific concepts, and answering specific questions via chatbot for real-time answers to routine inquiries to provide accurate and consistent responses and relevant resources. LLMs can support KOL engagement efforts by analyzing vast amounts of scientific literature, identifying emerging trends, and generating insights for targeted engagement strategies. LLMs can assist Medical Affairs teams in mining electronic health records, patient forums, and social media platforms to extract RWE from real-world data (RWD). By leveraging LLMs, Medical Affairs professionals can uncover trends, patient experiences, and treatment outcomes, ultimately contributing to evidence-based decision-making and the development of innovative healthcare solutions.
10. Improve Market Intelligence
The life sciences industry is a capital-intensive sector that thrives on mergers and acquisitions, licensing, alliances, royalties, and partnerships, with an annual exchange of over $225 billion. Investing in this industry requires a profound understanding of intricate biology, regulatory frameworks, competitive landscapes, patent considerations, medical and patient needs, as well as reimbursement and market access dynamics. Various types of deals, including tech transfer agreements, joint R&D collaborations, licensing agreements, and mergers and acquisitions, play a crucial role in fueling the industry's innovation and growth. Particularly, pharmaceutical companies often rely on external sourcing through partnerships with smaller biotech firms to build their pipelines. To meet the constant demand for market intelligence, leveraging an LLM can yield significant advantages. An LLM, equipped with advanced data analysis capabilities, can sift through vast volumes of information such as research papers, clinical trials, regulatory filings, and industry reports, enabling it to identify emerging trends, monitor market dynamics, and pinpoint areas of innovation for decision-makers. Its natural language processing capabilities enable the LLM to gather competitive intelligence, identify potential collaborations or partnerships, and gain deeper insights into patient needs and preferences. Further, the LLM's ability to comprehend complex scientific concepts and engage in conversational exchanges makes it an invaluable tool for strategists navigating the ever-changing life sciences landscape. By integrating an LLM into market intelligence efforts, organizations can make informed decisions, foster innovation, and gain a competitive edge within the life sciences sector.
There are opportunities to use LLMs to create significant value in many areas of the life sciences, yet the path to integrate LLMs into meaningful workflows is not a straightforward endeavor. An out-of-the-box implementation of ChatGPT or any other LLM will not work for these use cases. Concerns regarding security and privacy are of utmost importance, rendering off-the-shelf LLMs unsuitable. Additionally, ChatGPT's knowledge is limited to its training data, which only extends to 2021. The above referenced use cases require an LLM that is knowledgeable of real time emerging data sets, and with access to proprietary data sets. The biological complexity and high consequence environment of life sciences demands an LLM that can generate accurate and reliable insights linked to ground truth evidence. Given the propensity for out-of-the-box LLMs to generate false information, they cannot be considered a viable solution in life sciences.
To strike a balance between capturing the value of LLMs in life sciences and safeguarding data security, a viable approach involves internalizing open source LLM models, such as early GPT models, and repurposing them to operate within biological contexts using curated labeled datasets tailored to specific use cases. This strategy allows for the realization of LLM potential while addressing privacy and security requirements. By training and fine-tuning open source models to create proprietary models for specific tasks, concerns over privacy and security are mitigated, and access can be granted to recent and proprietary data. Furthermore, this approach enables the embedding of models in an environment that guards against hallucinations and ensures the ability to cite ground truth references.
In the rapidly evolving landscape of life sciences, the application of LLMs holds tremendous promise. While there are still challenges to overcome, such as ethical considerations and the need for robust validation, the potential to drive innovation and accelerate scientific discovery is immense. From extracting insights from scientific literature to analyzing genomic data and enhancing clinical trial design, these models offer sophisticated capabilities that can transform the industry. By training proprietary models using curated datasets and combining organizational knowledge with public information, organizations can leverage the power of LLMs while safeguarding proprietary data. It is clear that embracing LLMs in the life sciences industry can propel research, discovery, development, commercialization, and partnering to new levels of precision, efficiency, and quality.
Our team is excited to approach these challenges and is excited to work with our clients from strategy to productization, leading the way in the new frontier of AI in Life Sciences.
