Pharma Tech Outlook: Specials Magazine

The work of a GLP bioanalysis lab sits at the heart of drug development. Each study generates large volumes of data that must be transformed into formal reports before regulator review. Chemists configure studies inside Watson LIMS, run assays, and produce data. Report writers then step in to collate information, retrieve tables, and copy values into Word templates to generate method validation reports and sample analysis reports, which later move through rounds of review. This workflow, while established, creates significant friction. Data must be pulled from Watson in formats that require manual adjustment. Templates are often scattered across network folders, slowing access. Copy-paste transfers introduce the risk of error, forcing quality control reviewers to check line by line. As a result, reports that should move quickly can stretch into months. LabIntegrity was created to close these gaps. The company brings decades of experience in laboratory informatics and developed StudyDoc, a system that connects directly to the Watson Oracle database, structures the data, and delivers ready-to-use drafts. What once stalled in manual transfers now flows faster and more accurately, giving labs a path to quicker, regulator-ready reporting while easing the quality control burden that previously weighed teams down. “StudyDoc is true off-the-shelf software, unlike rigid tools. It adapts to any workflow and can generate full or partial reports, giving labs flexibility competitors cannot match,” says Larry E. Elvebak, president. StudyDoc: Practical Automation for Bioanalysis Reports StudyDoc works directly with Thermo Scientific Watson LIMS, the industry standard for bioanalysis study design and compliant data storage. The software retrieves raw study data from the Watson Oracle database, generates properly formatted tables, enters in the report body table-specific statistical variables (such as minimums and maximums of accuracies and precision) and reports stability conditions, automatically hyperlinks tables to appropriate references in the report, and builds a draft report at the push of a button. A writer can receive a report that is already 80 percent complete. Competitors often require rigid workflows where every study looks the same. StudyDoc, however, accepts the reality of CROs and labs that handle multiple study designs. Configuration settings adapt to each client’s approach, making the tool practical rather than prescriptive. The payoff is speed and accuracy. Instead of copying numbers from Excel into Word, writers can rely on automated insertion of validated data. Integrity is reinforced through the Document Control Manager, where all reports reside in a controlled shell with permissions, versioning, and audit trails. Reports meet 21 CFR Part 11 requirements and satisfy regulatory expectations for traceability.

CALICI began with a simple yet transformative vision to make artificial intelligence a practical, everyday tool for drug discovery. Founded in 2018, when AI was rapidly advancing, the company’s origins trace back to research at Baylor College of Medicine in Houston, Texas. While working on antiviral treatments for norovirus, the team tested hundreds of compounds using conventional in silico tools without success. A discussion with co-founder and AI specialist Daniel led to a new idea of training AI on the team’s past results to cut down the number of trials. Yet existing AI tools required complex coding skills, creating a barrier for many biologists. Calici set out to change that by developing a fully web-based platform that brought advanced AI directly to the laboratory scientist. The result is Pharmaco-Net^®, an online application that looks like a standard webpage but delivers sophisticated drug discovery capabilities. Guided by an AI agent called MediBot, users can upload protein structures, identify active sites, screen massive compound libraries, predict ADME/Tox properties, and refine molecular designs without requiring IT support. “Our approach makes AI drug discovery practical and immediate, turning digital predictions into real compounds ready for the lab,” says Choi Jae-Mun, CEO. Accessible, Actionable, and Built for Speed Many AI drug discovery companies run their predictive models internally and provide results to clients. Calici takes a different approach by offering its AI tools directly to researchers. This allows scientists to interact with the technology in real time, making the process more collaborative and transparent. The company also addresses a major industry challenge by turning AI-generated molecules into real, testable compounds. Standard generative models often create novel molecules that require months to synthesize or prove synthesizable altogether. Calici avoids these delays by focusing on stock chemical libraries from established vendor partners. If the AI recommends a compound, it is already available, synthesizable, and can be shipped to the client’s lab immediately. Behind this is a big data search AI strategy using reinforcement learning agents. Instead of scanning hundreds of millions of compounds one by one, these agents analyze the chemical space, identify patterns, and focus searches where the best candidates are most likely to be found. Pharmaco-Net^® draws from an extensive virtual compound library of over 600 million molecules, sourced through partnerships with TargetMol, Mcule, and Enamine, enabling rapid in-silico screening and real-world feasibility.