Advancements in molecular and genetic research are driving personalised, targeted therapies, and expanding treatment possibilities. In order to stay competitive, companies must find ways to adopt multimodal strategies, and this can require cross-functional collaboration and robust data management. Outsourcing is becoming increasingly important, which, in turn, makes secure data exchange and analysis critical.
Multimodal drug discovery incorporates diverse approaches, from small molecule to monoclonal antibodies, gene edition and CAR-T therapies, enabling treatment of previously "undruggable" targets. Combining therapies, such as targeted treatments with immunotherapies, enhances efficacy, especially in complex diseases such as cancer. Personalised medicine further tailors treatments based on genetic and disease-specific factors.
These strategies can present both risks and opportunities. Scientific, financial, and regulatory uncertainties are challenges, but, on the flipside, success can establish companies as leaders in new and exciting areas. Strategic partnerships across biotech, pharma, academia, and tech firms are key to accelerating innovation and mitigating risks.
The approach also extends beyond combining different drug types to include the integration of diagnostics, drug delivery systems, and AI-driven digital tools, which are becoming increasingly important for managing multimodal research data. Pharmaceutical companies are recognising the need for digital transformation across their value chains, investing in automation and digital solutions to improve efficiency, quality, and resilience.
Strategic considerations for pharma and biotech companies
Larger pharmaceutical companies, with their greater resources, are able to pursue multiple research modalities concurrently. Their particular challenges are in the optimisation of internal workflows, and managing outsourcing effectively. Smaller biotech companies, on the other hand, need to try to maximise the resources at hand, making tricky decisions about funding and prioritising specific drug development paths. They need to carefully analyse the potential return on investment for each modality, focusing on areas where they can excel and differentiate themselves. Organisations of all sizes, however, face the shared challenge of generating, organising, and analysing data efficiently and at scale.
Collaboration is an important part of developing new drug modalities. Partnerships between biotech startups, large pharmaceutical companies, academic institutions, and technology firms can bring together diverse skills, knowledge, and resources. For example, startups often have innovative ideas but may lack the resources to bring a product to market, while large companies have resources and market access but may need the fresh perspectives of smaller firms. Collaborations also enable risk-sharing, which is important when exploring new modalities. However, establishing and managing these partnerships, particularly when it comes to outsourcing, can present challenges around legal agreements, intellectual property, and data sharing. Efficient data exchange and management are key for successful collaborations, with FAIR (findable accessible, interoperable and reusable) data, and robust systems and expertise to manage and analyse the quantity of data generated in modern drug discovery.
The future of drug discovery
The future of drug discovery will be shaped by technological advancements, scientific breakthroughs, and evolving industry dynamics. AI and machine learning have the potential to accelerate the drug discovery process from target identification to clinical trial design. As mentioned, collaboration will be essential for accessing specialised skills and technologies, and those companies that prioritise data strategy and streamline communication and data exchange will have a competitive edge. Modern, accessible software technology plays an important role in managing the complex data and processes involved in drug development. Platforms that enable seamless integration, collaboration, and FAIR data management will be essential for future success.
Revvity Signals perspective
Many drug development technology initiatives fail because traditional IT systems, designed for structured commercial data, cannot sufficiently handle the variability of experimental research data. Success in digital transformation requires first understanding the research workflow, then implementing a systematic approach: capturing raw data from scientific instruments, converting it to structured, human/machine-readable formats, and automatically assembling it into analysable datasets suitable for AI-driven insights. This approach ensures that valuable experimental data can be effectively used for decision-making, while aiding collaboration across global research teams.
Revvity Signals aims to support multimodal drug discovery with a connected software solution that minimises the amount of software that scientists need to use, freeing them to focus on high-quality science. The Signals Research Suite is designed to work across modalities, recognising the balance between chemical and biological approaches. It provides tools and workflows tailored to individual and team needs, facilitating collaboration within and between organisations. For example, in antibody-drug conjugates (ADCs), which involve multiple modalities, the suite helps manage the complex data and workflows associated with each component, ultimately providing a holistic view of the final product.
The future of drug discovery is promising, but challenges remain. Regulatory hurdles, ethical considerations, and the need for sustainable practices must be addressed. However, with rapid scientific and technological advancements, and the support of advanced software solutions, the industry is well-positioned to overcome these challenges and deliver groundbreaking healthcare innovations.
