Closing the Loop - Part1: How AI and Digital Measures Bridge the Communication Gap Between Science and Technology

In the rapidly evolving landscape of drug development, the convergence between scientific rigor and technological innovation has never been more critical. The preclinical phase of drug development is a complex journey that demands a seamless integration of biological research and advanced computational tools. However, a significant gap often exists between the scientists driving the biological inquiries and the technologists developing tools such as artificial intelligence (AI) and digital measures. Bridging this gap is crucial to accelerating the validation and adoption of these emerging technologies.

Understanding the Gap Between Science and Technology

Divergent Disciplines

Scientists and technologists operate in distinct domains that, while overlapping, have different terminologies, methodologies, and objectives. Initially, organizations assumed that bringing technologists from other industries would be effective due to the transferability of skills. However, this approach often fell short. Technologists lacking domain-specific knowledge frequently struggled to apply their skills effectively within the highly specialized realm of drug development.

For example, a tech professional skilled in data analytics might excel at processing large datasets but may lack understanding of pharmacokinetics or the importance of regulatory compliance in handling biological data. In one case, a pharmaceutical company hired data scientists from the finance industry to develop predictive models for drug efficacy. Despite their technical prowess, these models were statistically robust but biologically irrelevant, as they failed to capture critical biological nuances.

To address this issue, pharmaceutical companies invested in in-house technology teams. However, these teams were often siloed, leading to the development of tools misaligned with the drug development workflow. The lack of integration resulted in solutions that were technically impressive but unusable in practice.

Scientists prioritize hypothesis-driven experiments aimed at exploring efficacy and safety, whereas technologists focus on creating automated systems and data-processing algorithms. This divergence can lead to misunderstandings and the underutilization of AI and digital measures in pharmaceutical research.

Communication Barriers

The use of discipline-specific jargon exacerbates the communication divide. For instance, while biologists refer to "gene expression levels," data scientists might discuss "numerical feature values," causing confusion. In one case, a team developing a genomic data analysis platform failed to align on terminology, delaying the project by several months as the tool did not meet the biologists' needs. Scientists often struggle to articulate their needs in terms that technologists can easily translate into functional AI tools, while technologists may have difficulty explaining the capabilities and limitations of AI and digital measures in a way that aligns with pharmaceutical goals. These challenges lead to inefficiencies, duplicated efforts, and missed opportunities for innovation.

Cultural Differences

The pharmaceutical industry often places high value on empirical evidence and rigorous validation, whereas the tech industry embraces rapid prototyping and scalability. For example, when adopting AI-driven drug discovery platforms, researchers are sometimes hesitant to trust black-box machine learning models that lack interpretability.

?At one pharmaceutical company, scientists resisted using an AI tool that provided opaque predictions, as it conflicted with their need for mechanistic insights. Another strategy attempted was developing criteria for new technologies by obtaining buy-in from the majority of potential users. However, this often became a never-ending loop, as achieving consensus among diverse stakeholders proved challenging. Projects stalled as a result, taking so long to move forward that the technology in question became outdated by the time it was ready for implementation.

Harnessing AI and Digital Measures to Bridge Communication Gap

In the table below, I share specific examples of how preclinical digital measures and the application of AI can help address the communication challenges outlined earlier.

Addressing the Cultural Divide

While AI and digital measures can help address communication barriers, fostering an integrated culture where scientists and technologists collaborate is essential. One approach is to establish cross-functional teams early in the process, involving both scientists and technologists in tool development. Regular workshops or "data clinics" can facilitate mutual understanding of both technological capabilities and biological constraints.

By ensuring that technology is developed with direct collaboration between scientists and technologists, and by ensuring technologists fully understand the biological objectives and scientific questions their tools will address, companies can foster the creation of fit-for-purpose AI models and data analysis platforms. This alignment accelerates drug development timelines, reduces communication-related delays, and enables more predictive modeling and data-driven decision-making earlier in the preclinical phase. When technologists grasp the specific hypotheses and endpoints under investigation, they can design machine learning algorithms and digital measures that more effectively solve relevant biological problems, ensuring that technological innovations provide actionable insights.

In blog 2, we’ll dive into strategies for bridging the communication gap between scientists and technologists—because who doesn’t love when lab coats and code come together? Stay tuned!?

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