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understanding immunogenicity at its core, immunogenicity refers to the ability of a substance, typically a drug or vaccine, to provoke an immune response within the body. it's the biological equivalent of setting off alarm bells. the stronger the response, the louder these alarms ring. in the case of vaccines, it is required for proper functioning of the vaccine: inducing an immune response and creating immunological memory. however, in the context of therapeutics, and particularly biotherapeutics, an unwanted immune response can potentially reduce the drug's efficacy or even lead to adverse effects. in pharma, the watchful eyes of agencies such as the fda and ema ensure that only the safest and most effective drugs make their way to patients; they require immunogenicity testing data before approving clinical trials and market access. these bodies necessitate stringent immunogenicity testing, especially for biosimilars, where it's essential to demonstrate that the biosimilar product has no increased immunogenicity risk compared to the reference product (1 ema), (2 fda). the interaction between the body's immune system and biologic drugs, such as monoclonal antibodies, can result in unexpected and adverse outcomes. cases have been reported where anti-drug antibodies (ada) led to lower drug levels and therapeutic failures, such as in the use of anti-tnf therapies, where patient immune responses occasionally reduced drug efficacy (3). beyond monoclonal antibodies, other biologic drugs, like enzyme replacement therapies and fusion proteins, also demonstrate variability in patient responses due to immunogenicity. in some instances, enzyme replacement therapies have been less effective because of immune responses that neutralize the therapeutic enzymes. similarly, fusion proteins used in treatments have shown varied efficacy, potentially linked to the formation of adas. the critical nature of immunogenicity testing is underscored by these examples, highlighting its role in ensuring drug safety and efficacy across a broader range of biologic treatments.  the challenge is to know beforehand whether an immune response will develop, ie the immunogenicity of a compound.   a deep dive into immunogenicity assessment of therapeutic antibodies  researchers rely on empirical analyses to comprehend the immune system's intricate interactions with external agents. immunogenicity testing is the lens that magnifies this interaction, revealing the nuances that can determine a drug's success or failure.  empirical analyses in immunogenicity assessments are informative but come with notable limitations. these analyses are often time-consuming, posing challenges to rapid drug development. early-phase clinical testing usually involves small sample sizes, which restricts the broad applicability of the results. pre-clinical tests, typically performed on animals, have limited relevance to human responses, primarily due to small sample sizes and interspecies differences. additionally, in vitro  tests using human materials do not fully encompass the diversity and complexity of the human immune system. moreover, they often require substantial time, resources, and materials. these issues highlight the need for more sophisticated methodologies that integrate human genetic variation for better prediction of drug candidates' efficacy. furthermore, the ability to evaluate the outputs from phage libraries during the discovery stage and optimization strategies like humanizations, developability, and affinity maturation can add significant value. being able to analyzing these strategies' impact on immunogenicity, with novel tools , may enhance the precision of these high throughput methods. . the emergence of in silico in immunogenicity screening with the dawn of the digital age, computational methods have become integral to immunogenicity testing.  in silico testing, grounded in computer simulations, introduces an innovative and less resource-intensive approach. however, it's important to understand that despite their advancements, in silico methods are not entirely predictive. there remains a grey area of uncertainty that can only be fully understood through experimental and clinical testing with actual patients. this underscores the importance of a multifaceted approach that combines computational predictions with empirical experimental and clinical data to comprehensively assess a drug's immunogenicity.   predictive role immunogenicity testing is integral to drug development, serving both retrospective and predictive purposes.  in silico analyses utilizing artificial intelligence and computational models to forecast a drug's behavior within the body can be used both in early and late stages of drug development. these predictions can also guide subsequent in vitro analyses, where the drug's cellular interactions are studied in a controlled laboratory environment. as a final step, traditionally immunogenicity monitoring in patients is crucial for regulatory approval.  the future of drug development envisions an expanded role for in silico testing through the combination with experimental and clinical data, to enhance the accuracy of predictive immunogenicity. this approach aims to refine predictions about a drug's safety and effectiveness before clinical trials, potentially streamlining the drug approval process. by understanding how a drug interacts with the immune system, researchers can anticipate possible reactions, optimize treatment strategies, and monitor patients throughout the process. understanding a drug's potential immunogenicity can inform dosing strategies, patient monitoring, and risk management. for instance, dose adjustments or alternative therapies might be considered if a particular population is likely to develop adas against a drug early on.   traditional vs. in silico methods: a comparative analysis traditional in vitro  methods, despite being time-intensive, offer direct insights from real-world biological interactions. however, it's important to recognize the limitations in the reliability of these methods, especially concerning in vitro wet lab tests used to determine a molecule's immunogenicity in humans. these tests often fall into a grey area in terms of their predictive accuracy for human responses. given this, the potential benefits of in silico analyses become more pronounced. in silico methods can complement traditional approaches by providing additional predictive insights, particularly in the early stages of drug development where empirical data might be limited. this integration of computational analyses can help identify potential immunogenic issues earlier in the drug development process, aiding in the efficient design of subsequent empirical studies. in silico methods, with their rapid processing and efficiency, are ideal for initial screenings, large datasets, and iterative testing. large amounts of hits can already be screened in the discovery stage and repeated when lead candidates are chosen and further engineered. the advantage of in silico methodologies lies in their capacity for high throughput analysis and quick turn-around times. traditional testing methods, while necessary for regulatory approval, present challenges in high throughput analysis due to their reliance on specialized reagents, materials, and equipment. these requirements not only incur substantial costs but also necessitate significant human expertise and logistical arrangements for sample storage. on the other hand, in silico testing, grounded in digital prowess, sees the majority of its costs stemming from software and hardware acquisition, personnel and maintenance.  by employing in silico techniques, it becomes feasible to rapidly screen and eliminate unsuitable drug candidates early in the discovery and development process. this early-stage screening significantly enhances the efficiency of the drug development pipeline by focusing resources and efforts on the most promising candidates. consequently, the real cost-saving potential of in silico analysis emerges from its ability to streamline the candidate selection process, ensuring that only the most viable leads progress to costly traditional testing and clinical trials.   advantages of in silico in immunogenicity screening in silico immunogenicity testing is transforming drug development by offering rapid insights and early triaging, which is instrumental in de-risking the pipeline and reducing attrition costs. these methodologies can convert extensive research timelines into days or hours, vastly accelerating the early stages of drug discovery and validation. as in silico testing minimizes the need for extensive testing of high number of candidates in vitro, its true value lies in its ability to facilitate early-stage decision-making. this early triaging helps identify potential failures before significant investment, thereby lowering the financial risks associated with drug development.   in silico immunogenicity screening in decision-making employing an in silico platform enables researchers to thoroughly investigate the molecular structure, function, and potential interactions of proteins at an early stage. this process aids in the early triaging of drug candidates by identifying subtle variations that could affect therapeutic efficacy or safety. additionally, the insights gleaned from in silico analyses can inform our understanding of how these molecular characteristics may relate to clinical outcomes, enriching the knowledge base from which we draw predictions about a drug's performance in real-world.   de-risking with informed lead nomination the earliest stages of therapeutic development hinge on selecting the right lead candidates—molecules or compounds that exhibit the potential for longevity. making an informed choice at this stage can be the difference between success and failure. in-depth analysis such as immunogenicity analysis aims to validate that selected leads are effective and exhibit a high safety profile. to benefit from the potential and efficiency of in silico methods in drug discovery, it's crucial to choose the right platform to realize these advantages. this is where lensai integrated intelligence technology comes into play. introducing the future of protein analysis and immunogenicity screening: lensai. powered by the revolutionary hyft technology, lensai is not just another tool; it's a game-changer designed for unmatched throughput, lightning-fast speeds, and accuracy. streamline your workflow, achieve better results, and stay ahead in the ever-evolving world of drug discovery. experience the unmatched potency of lensai integrated intelligence technology.   learn more: lensai in silico immunogenicity screening   understanding immunogenicity and its intricacies is fundamental for any researcher in the field. traditional methods, while not entirely predictive, have been the cornerstone of immunogenicity testing. however, the integration of in silico techniques is enhancing the landscape, offering speed and efficiency that complement existing methods. at mindwalk we foresee the future of immunogenicity testing in a synergistic approach that strategically combines in silico with in vitro methods. in silico  immunogenicity prediction can be applied in a high throughput way during the early discovery stages  but also later in the development cycle when engineering lead candidates to provide deeper insights and optimize outcomes. for the modern researcher, employing both traditional and in silico methods is the key to unlocking the next frontier in drug discovery and development. looking ahead, in silico is geared towards becoming a cornerstone for future drug development, paving the way for better therapies.   references: ema guideline on immunogenicity assessment of therapeutic proteins fda guidance for industry immunogenicity assessment for therapeutic protein products anti-tnf therapy and immunogenicity in inflammatory bowel diseases: a translational approach

in a recent article on knowledge graphs and large language models (llms) in drug discovery, we noted that despite the transformative potential of llms in drug discovery, there were several critical challenges that have to be addressed in order to ensure that these technologies conform to the rigorous standards demanded by life sciences research. synergizing knowledge graphs with llms into one bidirectional data- and knowledge-based reasoning framework addresses several concerns related to hallucinations and lack of interpretability. however, that still leaves the challenge of enabling llms access to external data sources that address their limitation with respect to factual accuracy and up-to-date knowledge recall. retrieval-augmented generation (rag), together with knowledge graphs and llms, is the third critical node on the trifecta of techniques required for the robust and reliable integration of the transformative potential of language models into drug discovery pipelines. why retrieval-augmented generation? one of the key limitations of general-purpose llms is their training data cutoff, which essentially means that their responses to queries are typically out of step with the rapidly evolving nature of information. this is a serious drawback, especially in fast-paced domains like life sciences research. retrieval-augmented generation enables biomedical research pipelines to optimize llm output by: grounding the language model on external sources of targeted and up-to-date knowledge to constantly refresh llms' internal representation of information without having to completely retrain the model. this ensures that responses are based on the most current data and are more contextually relevant. providing access to the model's information so that responses can be validated for accuracy and sources, ensuring that its claims can be checked for relevance and accuracy. in short, retrieval-augmented generation provides the framework necessary to augment the recency, accuracy, and interpretability of llm-generated information. how does retrieval-augmented generation work? retrieval augmented generation is a natural language processing (nlp) approach that combines elements of both information retrieval and text generation models to enhance the performance of knowledge-intensive tasks. the retrieval component aggregates information relevant to specific queries from a predefined set of documents or knowledge sources which then serves as the context for the generation model. once the information has been retrieved, it is combined with the input context to create an integrated context containing both the original query and the relevant retrieved information. this integrated context is then fed into a generation model to generate an accurate, coherent, and contextually appropriate response based on both pre-trained knowledge and retrieved query-specific information. the rag approach gives life sciences research teams more control over grounding data used by a biomedical llm by honing it on enterprise- and domain-specific knowledge sources. it also enables the integration of a range of external data sources, such as document repositories, databases, or apis, that are most relevant to enhancing model response to a query. the value of rag in biomedical research conceptually, the retrieve+generate model’s capabilities in terms of dealing with dynamic external information sources, minimizing hallucinations, and enhancing interpretability make it a natural and complementary fit to augment the performance of biollms. in order to quantify this augmentation in performance, a recent research effort evaluated the ability of a retrieval-augmented generative agent in biomedical question-answering vis-a-vis llms (gpt-3.5/4), state-of-the-art commercial tools (elicit, scite, and perplexity) and humans (biomedical researchers). the rag agent, paperqa, was first evaluated against a standard multiple-choice llm-evaluation dataset, pubmedqa, with the provided context removed to test the agents’ ability to retrieve information. in this case, the rag agent beats gpt-4 by 30 points (57.9% to 86.3%). next, the researchers constructed a more complex and more contemporary dataset (litqa), based on more recent full-text research papers outside the bounds of llm’s pre-training data, to compare the integrated abilities of paperqa, llms and human researchers to retrieve the right information and to generate an accurate answer based on that information. again, the rag agent outperformed both pre-trained llms and commercial tools with overall accuracy (69.5%) and precision (87.9%) scores that were on par with biomedical researchers. more importantly, the rag model produced zero hallucinated citations compared to llms (40-60%). despite being just a narrow evaluation of the performance of the retrieval+generation approach in biomedical qa, the above research does demonstrate the significantly enhanced value that rag+biollm can deliver compared to purely generative ai. the combined sophistication of retrieval and generation models can be harnessed to enhance the accuracy and efficiency of a range of processes across the drug discovery and development pipeline. retrieval-augmented generation in drug discovery in the context of drug discovery, rag can be applied to a range of tasks, from literature reviews to biomolecule design. currently, generative models have demonstrated potential for de novo molecular design but are still hampered by their inability to integrate multimodal information or provide interpretability. the rag framework can facilitate the retrieval of multimodal information, from a range of sources, such as chemical databases, biological data, clinical trials, images, etc., that can significantly augment generative molecular design. the same expanded retrieval + augmented generation template applies to a whole range of applications in drug discovery like, for example, compound design (retrieve compounds/ properties and generate improvements/ new properties), drug-target interaction prediction (retrieve known drug-target interactions and generate potential interactions between new compounds and specific targets. adverse effects prediction (retrieve known adverse and generate modifications to eliminate effects). etc. the template even applies to several sub-processes/-tasks within drug discovery to leverage a broader swathe of existing knowledge to generate novel, reliable, and actionable insights. in target validation, for example, retrieval-augmented generation can enable the comprehensive generative analysis of a target of interest based on an extensive review of all existing knowledge about the target, expression patterns and functional roles of the target, known binding sites, pertinent biological pathways and networks, potential biomarkers, etc. in short, the more efficient and scalable retrieval of timely information ensures that generative models are grounded in factual, sourceable knowledge, a combination with limitless potential to transform drug discovery. an integrated approach to retrieval-augmented generation retrieval-augmented generation addresses several of the critical limitations and augments the generative capabilities of biollms. however, there are additional design rules and multiple technological profiles that have to come together to successfully address the specific requirements and challenges of life sciences research. our lensai™ integrated intelligence platform seamlessly unifies the semantic proficiency of knowledge graphs, the versatile information retrieval capabilities of retrieval-augmented generation, and the reasoning capabilities of large language models to reinvent the understanding-retrieve-generate cycle in biomedical research. our unified approach empowers researchers to query a harmonized life science knowledge layer that integrates unstructured information & ontologies into a knowledge graph. a semantic-first approach enables a more accurate understanding of research queries, which in turn results in the retrieval of content that is most pertinent to the query. the platform also integrates retrieval-augmented generation with structured biomedical data from our hyft technology to enhance the accuracy of generated responses. and finally, lensai combines deep learning llms with neuro-symbolic logic techniques to deliver comprehensive and interpretable outcomes for inquiries. to experience this unified solution in action, please contact us here.

over the past year, we have looked at drug discovery and development from several different perspectives. for instance, we looked at the big data frenzy in biopharma, as zettabytes of sequencing, real-world and textual data (rwd) pile up and stress the data integration and analytic capabilities of conventional solutions. we also discussed how the time-consuming, cost-intensive, low productivity characteristics of the prevalent roi-focused model of development have an adverse impact not just on commercial viability in the pharma industry but on the entire healthcare ecosystem. then we saw how antibody drug discovery processes continued to be cited as the biggest challenge in therapeutic r&d even as the industry was pivoting to biologics and mabs. no matter the context or frame of reference, the focus inevitably turns to how ai technologies can transform the entire drug discovery and development process, from research to clinical trials. biopharma companies have traditionally been slow to adopt innovative technologies like ai and the cloud. today, however, digital innovation has become an industry-wide priority with drug development expected to be the most impacted by smart technologies. from application-centric to data-centric ai technologies have a range of applications across the drug discovery and development pipeline, from opening up new insights into biological systems and diseases to streamlining drug design to optimizing clinical trials. despite the wide-ranging potential of ai-driven transformation in biopharma, the process does entail some complex challenges. the most fundamental challenge will be to make the transformative shift from an application-centric to a data-centric culture, where data and metadata are operationalized at scale and across the entire drug design and development value chain. however, creating a data-centric culture in drug development comes with its unique set of data-related challenges. to start with there is the sheer scale of data that will require a scalable architecture in order to be efficient and cost-effective. most of this data is often distributed across disparate silos with unique storage practices, quality procedures, and naming and labeling conventions. then there is the issue of different data modalities, from mr or ct scans to unstructured clinical notes, that have to be extracted, transformed, and curated at scale for unified analysis. and finally, the level of regulatory scrutiny on sensitive biomedical data means that there is this constant tension between enabling collaboration and ensuring compliance. therefore, creating a strong data foundation that accounts for all these complexities in biopharma data management and analysis will be critical to ensuring the successful adoption of ai in drug development. three key requisites for an ai-ready data foundation successful ai adoption in drug development will depend on the creation of a data foundation that addresses these three key requirements. accessibility data accessibility is a key characteristic of ai leaders irrespective of sector. in order to ensure effective and productive data democratization, organizations need to enable access to data distributed across complex technology environments spanning multiple internal and external stakeholders and partners. a key caveat of accessibility is that the data provided should be contextual to the analytical needs of specific data users and consumers. a modern cloud-based and connected enterprise data and ai platform designed as a “one-stop-shop” for all drug design and development-related data products with ready-to-use analytical models will be critical to ensuring broader and deeper data accessibility for all users. data management and governance the quality of any data ecosystem is determined by the data management and governance frameworks that ensure that relevant information is accessible to the right people at the right time. at the same time, these frameworks must also be capable of protecting confidential information, ensuring regulatory compliance, and facilitating the ethical and responsible use of ai. therefore, the key focus of data management and governance will be to consistently ensure the highest quality of data across all systems and platforms as well as full transparency and traceability in the acquisition and application of data. ux and usability successful ai adoption will require a data foundation that streamlines accessibility and prioritizes ux and usability. apart from democratizing access, the emphasis should also be on ensuring that even non-technical users are able to use data effectively and efficiently. different users often consume the same datasets from completely different perspectives. the key, therefore, is to provide a range of tools and features that help every user customize the experience to their specific roles and interests. apart from creating the right data foundation, technology partnerships can also help accelerate the shift from an application-centric to a data-centric approach to ai adoption. in fact, a 2018 gartner report advised organizations to explore vendor offerings as a foundational approach to jump-start their efforts to make productive use of ai. more recently, pharma-technology partnerships have emerged as the fastest-moving model for externalizing innovation in ai-enabled drug discovery. according to a recent roots analysis report on the ai-based drug discovery market, partnership activity in the pharmaceutical industry has grown at a cagr of 50%, between 2015 and 2021, with a majority of the deals focused on research and development. so with that trend as background, here’s a quick look at how a data-centric, full-service biotherapeutic platform can accelerate biopharma’s shift to an ai-first drug discovery model. the lensai™ approach to data-centric drug development our approach to biotherapeutic research places data at the very core of a dynamic network of biological and artificial intelligence technologies. with our lensai platform, we have created a google-like solution for the entire biosphere, organizing it into a multidimensional network of 660 million data objects with multiple layers of information about sequence, syntax, and protein structure. this “one-stop-shop” model enables researchers to seamlessly access all raw sequence data. in addition, hyfts®, our universal framework for organizing all biological data, allows easy, one-click integration of all other research-relevant data from across public and proprietary data repositories. researchers can then leverage the power of the lensai integrated intelligence platform to integrate unstructured data from text-based knowledge sources such as scientific journals, ehrs, clinical notes, etc. here again, researchers have the ability to expand the core knowledge base, containing over 33 million abstracts from the pubmed biomedical literature database, by integrating data from multiple sources and knowledge domains, including proprietary databases. around this multi-source, multi-domain, data-centric core, we have designed next-generation ai technologies that can instantly and concurrently convert these vast volumes of text, sequence, and protein structure data into meaningful knowledge that can transform drug discovery and development.