Cognitive Search for Life Sciences R&D: How Pfizer, AstraZeneca & GSK Accelerate Drug Discovery

The $1.3 Billion Question in Drug Discovery

The average cost of bringing a new drug to market is $1.3 billion, according to the London School of Economics. Every delay in the discovery and development process adds to this figure — and the most pervasive source of delay is not scientific complexity. It is an information problem.

Pharmaceutical and life sciences organizations generate enormous volumes of research data across Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), Chromatography Data Systems (CDS), Laboratory Execution Systems (LES), clinical trial databases, regulatory submissions, scientific literature databases, patent repositories, and internal knowledge bases. The critical knowledge that could accelerate the next breakthrough — a prior experiment’s results, a relevant compound already tested and ruled out, an expert colleague who has worked on this exact target — is almost always somewhere in the organization. The challenge is that it cannot be found.

The result is costly duplication: researchers unknowingly repeat experiments already performed, pursue compound candidates already eliminated, and fail to connect with internal experts who could redirect their efforts. In an industry where lab experimentation is both the most expensive and most time-consuming part of the development process, the inability to leverage existing knowledge is one of the highest-cost inefficiencies a pharma organization faces.

Sinequa’s Cognitive Search & Analytics platform solves this — connecting all of a life sciences organization’s data sources into a single, intelligent, AI-powered knowledge layer that every scientist, researcher, and regulatory professional can access from one interface.

What This Whitepaper Covers

Accelerating the Life Sciences with Cognitive Search is Sinequa’s definitive guide to how pharmaceutical and life sciences organizations are using AI-powered enterprise search to transform every phase of the drug development lifecycle. The whitepaper covers:

Drug Discovery and Lab Data Management Lab experimentation is the foundation of drug discovery — and reproducibility, the ability to reliably connect the results of past experiments to new research directions, is its cornerstone. Sinequa connects all lab data repositories — ELNs, LIMS, CDS, LES — into a unified searchable layer, enabling researchers to find every prior experiment on a target compound, understand what has already been tested and why, and build new research on the full foundation of institutional knowledge rather than only what is immediately visible. This eliminates duplicate experimentation, prevents researchers from pursuing dead ends already identified, and ensures that when a key colleague is unavailable, their knowledge remains accessible.

Enhancing Search Serendipity Across Internal and External Sources Drug discovery involves not just finding known results but discovering unexpected connections — between compounds, targets, mechanisms, and research findings — that can open new development pathways. Sinequa ingests and normalizes data from both internal repositories and external sources (scientific publications, patent databases, competitive intelligence, market research) simultaneously, connecting 80% unstructured data using NLP, semantic analysis, text mining, and machine learning. With support for over 350 file formats, no relevant data source is out of reach. A unique capability is the automatic surfacing of internal subject matter experts alongside relevant documents — enabling researchers to connect with the right colleague as quickly as they find the right document.

Clinical Development and Trial Management Clinical statisticians gain a holistic view across all experimental trial data and reports through a single interface. Sinequa enables them to uncover previously unseen connections between subjects, modifiers, and side effects — locating subjects by symptoms and filtering across drugs and studies with precision. This accelerates trial design, improves subject identification, and enables the kind of cross-study analysis that drives faster, better-evidenced clinical decisions.

Regulatory Affairs Regulatory teams can search all prior submissions and agency responses — to the FDA, EMA, PMDA, and other global regulators — from a single interface. This prevents the costly and time-consuming process of collecting information from scratch for every new submission, ensures regulatory responses are consistent across time and geography, and supports faster New Drug Application preparation through access to the complete institutional history of prior regulatory engagement.

Medical Information and Field Teams Medical information groups gain a uniform view of field data previously distributed across discrete silos, enabling employees to deliver consistent, accurate scientific and clinical knowledge about the company’s products. With a single portal, the risk of regulatory conflicts caused by inconsistent information is dramatically reduced.

Manufacturing and Lab Operations Even at the manufacturing level, pharmaceutical personnel benefit from a single, mobile-enabled point of entry to all relevant documentation — standard operating procedures, equipment records, maintenance histories, and process specifications — accessible via smartphone or tablet, searchable by keyword or QR code. This eliminates paper-based processes and gives facility operators, technicians, and engineers the information they need at the point of need.