Life Sciences & Pharma.
Clinical trial management systems, regulatory submission platforms, and drug discovery AI, an emerging practice we're building toward, not yet claiming deep delivery history in.
Clinical trial management systems, regulatory submission platforms, and drug discovery AI, an emerging practice we're building toward, not yet claiming deep delivery history in.
Life sciences and pharma technology sits under some of the strictest validation and regulatory requirements in software, 21 CFR Part 11, EU Annex 11, and the submission formats specific agencies require. This is a vertical DashMindsIQ is building capability toward rather than one where we currently have extensive delivered engagements, and we say so directly rather than overstate our track record. The topics below describe the systems we are equipped to build once a client engagement is confirmed.
Clinical trial management systems support the operational management of clinical research studies, tracking participant enrolment and eligibility, managing protocol versions and amendments, scheduling and recording site visits, collecting adverse event reports, and maintaining the audit trail that good clinical practice (GCP) regulations require throughout a trial's lifecycle. We build CTMS platforms and integrate them with eCRF (electronic case report form) systems, randomisation and trial supply management tools, and the regulatory submission platforms that consume the data they generate, with the validation and data integrity controls that 21 CFR Part 11 and EU Annex 11 compliance requires.
Pharmaceutical regulatory submissions, to the CDSCO in India, the FDA in the US, the EMA in Europe, or other national agencies, involve assembling large volumes of clinical, non-clinical, and quality data into structured dossiers that meet precise format specifications (eCTD for most major markets) and managing the review correspondence, query responses, and label negotiations that follow. We build regulatory information management platforms that manage document authoring workflows, version control, submission compilation, agency correspondence tracking, and the product lifecycle data management that keeps regulatory records current across all markets in which a product is approved.
AI is transforming the early stages of drug discovery by making it possible to screen vastly larger chemical spaces than traditional experimental approaches, predict the biological activity and toxicity properties of candidate molecules before synthesis, and identify novel targets by mining the growing body of genomic, proteomic, and phenotypic data. We build drug discovery AI tools covering molecular property prediction (using graph neural networks and transformer-based molecular representations), virtual screening pipelines, target identification from multi-omics data, and the data infrastructure and model management systems that allow computational chemistry and biology teams to iterate rapidly on hypotheses without being limited by infrastructure constraints.