The fusion of artificial intelligence (AI) with multi-omics – a comprehensive study encompassing genomics, transcriptomics, proteomics, metabolomics, and other related fields – is heralding an era of speedier, more efficient drug discovery. AI has been disrupting various sectors, but its potential in revolutionizing the drug discovery process – traditionally a complex, time-consuming, and expensive undertaking – is garnering significant attention. The integration of AI with multi-omics approaches is enhancing the predictive power of drug discovery algorithms, minimizing risks, and expediting the journey from the bench to the bedside.

Understanding the Multi-Omics Landscape

The surge of multi-omics studies has been stimulated by the advent of high-throughput sequencing technologies and advanced bioinformatics tools. This approach allows for a comprehensive exploration of biological systems by investigating the dynamic relationships between various molecular entities including genes, RNAs, proteins, and metabolites, and therefore understanding disease mechanisms at the molecular level.  However, the sheer volume and complexity of data generated pose a significant challenge. This is where AI swoops in, transforming massive datasets into meaningful, actionable insights.

Drug Discovery Challenges

Traditional drug discovery methods often fall short when faced with the challenges presented by complex diseases. Single-target drugs, which focus on modulating the activity of a specific protein or gene, may not be effective in treating diseases that involve multiple molecular players and pathways. Therefore tackling complex diseases – conditions like cancer, autoimmune diseases, and neurological disorders, which are typically characterized by multifactorial causes and a high degree of heterogeneity is very challenging. Moreover, the high degree of variability in disease manifestations makes it difficult to predict patients’ responses to these therapies.

Harnessing the Power of AI

Machine learning (ML), a subset of AI, is particularly adept at recognizing patterns within large datasets – an invaluable asset in the field of multi-omics. Deep learning algorithms can be trained to unearth correlations between multiple biological layers, such as genotypes and phenotypes, and how they interact to create disease states. AI-based models can then use these correlations to predict a drug target and how a given drug compound would affect a specific biological system, forecasting the drug’s efficacy and potential side effects.

Moreover, AI can substantially enhance the power of precision medicine. By learning from multi-omics data, AI can help design patient-specific therapeutic regimens, addressing the underlying genetic and metabolic drivers of the disease instead of the symptoms alone.

Multi-omics and AI are beginning to show immense potential in the fight against complex diseases such as cancer. By using AI to interpret multi-omics data, researchers can gain a deeper understanding of the multi-faceted nature of various cancer types and design more targeted and effective treatments.

Companies utilizing AI in multi-omics models

1. Verge Genomics

Verge Genomics is a leading-edge biotech company that employs artificial intelligence and human data to pioneer drug discovery. The firm is dedicated to addressing complex diseases that currently have a high level of unmet need, such as ALS and Parkinson’s disease. Verge’s innovation centers around its AI-powered CONVERGE™ platform, which leverages vast multi-omic data sets from patient disease tissues. 

Recently, Verge announced a significant milestone: the successful completion of a Phase 1 clinical trial for VRG50635, a promising PIKfyve inhibitor treatment for ALS. This is one of the first drugs discovered and developed entirely through an AI-enabled platform, including target identification. The trial results indicated a favorable safety, tolerability, and pharmacokinetic profile.

Verge Genomics, founded in 2015, right now accounts for $134.1M of investments, with the last round B raised in 2021. 

2. MultiOmic Heath

Multiomic Health, a London-based venture, is focusing on precision medicine for metabolic syndrome-related conditions, including cardiovascular disease, type 2 diabetes, chronic kidney disease, and non-alcoholic fatty liver disease. The company deploys computational systems biology modeling and AI-enabled predictive analytics on patient data to identify unique endotypes and discover novel drug targets.

Multiomic Health recently raised £5 million (US$6.2 million) in a seed funding round led by Hoxton Ventures with contributions from Ada Ventures, MMC Ventures, and Verve Ventures, amounting to $8.8M in total  funding.  The funding will be utilized to validate the company’s proprietary MOHSAIC® platform, which combines AI techniques, systems biology simulations, and wet lab experiments in the field of diabetic kidney disease.

The global prevalence of metabolic syndrome-related conditions, affecting one in three adults, represented nearly $2 trillion in healthcare spending in 2019, with projections surpassing $5.5 trillion by 2040. Multiomic Health’s approach, if successful, may potentially unlock a significant portion of this vast market. 

3. Metabolon

Metabolon, Inc., a global leader in the field of metabolomics, has announced the successful procurement of $25 M in additional equity financing,  bringing their total funding amount to $184.7 M The firm anticipates using these funds for general corporate purposes to expedite its commercialization activities and further advance its research and development roadmap.

The firm’s primary area of expertise lies in the development of metabolomics solutions designed to facilitate research, diagnostic, therapeutic development, and precision medicine applications. Metabolomics involves the large-scale study of all small molecules within a biological system, which provides an extensive understanding of an individual’s health beyond just their genetic variation.

Metabolon’s technology measures thousands of discrete chemical signals that form biological pathways in the body. These measurements can reveal essential biomarkers enabling a better understanding of a drug’s mechanism of action, pharmacodynamics, and safety profile. Additionally, they can give insights into individual responses to therapy. 

4. Via Scientific

Via Scientific, an AI company from Cambridge, has launched Foundry, a multi-omics accelerator platform created to hasten scientific breakthroughs. The platform, developed at UMass Chan Medical School, automates complex data work associated with multi-omics research, simplifying it for scientists. Foundry features a drag-and-drop pipeline, reusable data, and customizable analytics. This platform eliminates the need for coding, freeing researchers to focus on scientific insights and therapeutic breakthroughs. The company, founded in 2022, was publicly launched in February 2023 after a year in stealth mode. Via Scientific caters to biotech, pharmaceutical companies, research institutes, and universities, and is continuously enhancing Foundry’s AI capabilities. They are currently expanding and inviting applications from skilled engineers, AI experts, and bioinformaticians.

5. Tempus

Tempus is a technology company, valued at $10 billion, that is spearheading the use of artificial intelligence in healthcare, particularly in the realm of precision medicine. It has developed an operating system to make multimodal data accessible and useful, thus providing AI-enabled precision medicine solutions to physicians for personalized patient care. This multimodal platform incorporates diverse data types such as genomics, transcriptomics, and imaging to create comprehensive patient profiles. Tempus’ vast library of multimodal data is one of the world’s largest, enabling a holistic understanding of patient populations.

Recently, Tempus has furthered its reach by establishing strategic partnerships with global pharmaceutical leaders. These collaborations, including significant alliances with Actuate Therapeutics and Pfizer, are designed to harness Tempus’ multimodal and multi-omics capabilities to enhance drug development, particularly in oncology. The collaboration with Actuate Therapeutics supports its ongoing study of elraglusib, a cancer drug, using Tempus’ multi-omics and multimodal data approach to enhance the discovery and validation of novel drug targets and biomarkers.

6. Ocean Genomics

Ocean Genomics, a Pittsburgh-based technology and AI company, specializes in developing advanced computational platforms that assist biopharma companies in discovering and developing more effective diagnostics and therapeutics. Their software focuses on understanding the changes and variants in mRNA to enable a more accurate prediction of a patient’s biological response to drugs. The collaboration with Ocean Genomics would give biotech companies access to personalized in-silico drug discovery platform.

The company has recently attracted a strategic investment from Accenture through Accenture Ventures. This partnership aims to accelerate the development of individualized treatments and disease interventions. The collaboration also makes Ocean Genomics part of Accenture Ventures’ Project Spotlight, an initiative that connects emerging tech startups with large-scale businesses for strategic innovation.

Ocean Genomics’ technology integrates gene expression, molecular features, clinical and genomics data, and advanced AI algorithms. Their technology platform, DeepSea™, provides deeply characterized transcriptomes, curated metadata, and pre-trained AI models. Their focus is on making data-driven decisions and increasing the probability of technical and clinical success at every step. The specific terms of the Accenture transaction, however, were not disclosed.

In conclusion, the combination of artificial intelligence and multi-omics aims to solve complex problems related to understanding disease mechanisms, target identification, and predicting potential therapeutic drug efficacy. AI’s ability to derive actionable insights from enormous and complex datasets significantly reduces the risk, cost, and time associated with traditional drug discovery methods.

While challenges related to data privacy, standardization, and integration still persist, the growing interest from investors and strategic partnerships with pharmaceutical giants indicate strong confidence in the potential of AI and multi-omics. As the technology evolves and matures, it is anticipated that the integration of AI with multi-omics will become a standard feature in the healthcare landscape. As we witness the blending of AI and multi-omics, we can expect a significant shift in our approach towards healthcare, transforming it from a one-size-fits-all model to a more personalized, precision-driven one. This is truly an exciting time in the realm of biotechnology, and we look forward to reporting on the continued advancements and breakthroughs in this field.

Topics: Emerging Technologies   

• SEO Powered Content & PR Distribution. Get Amplified Today.
• EVM Finance. Unified Interface for Decentralized Finance. Access Here.
• Quantum Media Group. IR/PR Amplified. Access Here.
• PlatoAiStream. Web3 Data Intelligence. Knowledge Amplified. Access Here.
• Source: https://www.biopharmatrend.com/post/696-how-ai-driven-multi-omics-for-drug-discovery-is-reshaping-medicine/