Pharmaceutical Software Development

Pharmaceutical companies building drug lifecycle platforms, clinical trial systems, regulatory submission pipelines, and pharmacy management software need engineering teams who understand both GxP validation requirements and the data architecture that supports them. Scrums.com provides dedicated software engineering teams for pharmaceutical software development, deploying production-ready systems with full audit trail integrity, serialisation compliance, and the regulatory traceability that FDA 21 CFR Part 11, EU Annex 11, and GMP frameworks require.

Drug Lifecycle Management and Regulatory Compliance Architecture

Pharmaceutical companies building drug lifecycle platforms, clinical trial systems, regulatory submission pipelines, and pharmacy management software need engineering teams who understand both GxP validation requirements and the data architecture that supports them. Scrums.com provides dedicated software engineering teams for pharmaceutical software development, deploying production-ready systems with full audit trail integrity, serialisation compliance, and the regulatory traceability that FDA 21 CFR Part 11, EU Annex 11, and GMP frameworks require.

The core pattern is a compound entity model: a versioned master record (drug_products, study_protocol_versions, batch_records) paired with an append-only audit_events table that records every field change, user ID, timestamp, and reason_for_change. The current state of any regulated record is always derived from its event log, never mutated in place. This satisfies both 21 CFR Part 11 electronic record requirements and EU Annex 11 audit trail provisions without additional middleware.

Change control workflows use a typed state machine: DRAFT / IN_REVIEW / APPROVED / EFFECTIVE / SUPERSEDED / RETIRED. Each transition writes a change_control_events row. Approvals require a digital signature record (user_id, signature_meaning, signed_at, authentication_method) stored as a child row: signatures are never stored as field values on the parent record. Signature records are immutable; corrections create a new superseding record with a link to the voided original.

Document management links controlled documents to their parent entity through a document_links table. Each document version carries an effective_from/effective_to date range; queries against a point in time return the version that was current at that moment. This supports retrospective inspection without manual reconstruction of what the SOP said on a given date.

Clinical Data Collection and Electronic Trial Master File Architecture

Clinical trial systems require a data model that keeps raw subject data immutable while supporting query, audit, and regulatory export at any point in the trial lifecycle.

Subject data is captured through eCRF submissions stored in a subject_visit_data table: one row per field per visit, with subject_id, visit_id, field_id, raw_value, entry_user_id, entry_timestamp, and a source_document_reference. Corrections use a separate data_corrections table with original_row_id, corrected_value, correction_reason, corrector_id, and correction_timestamp: the original row is never modified. This is the direct implementation of ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate) data integrity principles.

Discrepancy management uses a discrepancy_queries table linked to the original data row, with a status machine (OPEN / ANSWERED / CLOSED | CANCELLED). Query resolution creates a resolution_events row, not an update to the discrepancy record. All resolution commentary is append-only.

The Electronic Trial Master File (eTMF) uses the DIA Reference Model zone and section taxonomy stored in a tmf_taxonomy table updateable without deployment. Documents are stored in object storage with checksums recorded at upload; the tmf_documents table stores metadata, zone/section classification, effective dates, and a status machine (EXPECTED / RECEIVED / QC_REVIEW / APPROVED | REJECTED). Missing document alerts are generated by comparing expected vs received at the study, site, and milestone level.

Randomisation and trial supply management keep blinding intact by separating the treatment_allocation table (blinded, accessible only to unblinded pharmacist role) from subject_assignments (subject ID and allocation reference only). Access control is enforced at the query layer, not only in application middleware.

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Serialisation, Supply Chain Track-and-Trace, and Cold Chain

Pharmaceutical serialisation compliance (EU FMD, DSCSA, and equivalent frameworks) requires event-driven track-and-trace architecture where every unit and case movement is recorded as an immutable event against a unique serial number.

The serialisation model has three tiers: saleable_units (SGTIN, expiry, batch), aggregation_records (parent-child relationship for cases and pallets), and serial_events (COMMISSIONED | PACKED | SHIPPED | RECEIVED | DISPENSED | DESTROYED | RECALLED). Each serial_events row records serial_number, event_type, location_id, trading_partner_id, timestamp, and transaction_reference. Serial numbers are never deleted; DESTROYED and RECALLED events are the terminal states in the status machine.

Verification endpoints against national medicines verification systems use an outbound_verification_log: request payload, response, latency, and outcome stored per call. Verification failures trigger a holds table entry; holds are released only through an explicit approval event. The verification integration uses an adapter pattern so new regulatory systems can be added without schema changes.

Cold chain monitoring uses an append-only temperature_readings table (logger_id, reading_timestamp, temperature_celsius, humidity_percent, location_id). The acceptable_ranges config table defines per-product or per-location thresholds. A stream processor evaluates each reading against applicable ranges and writes temperature_excursion_events when thresholds are breached. Excursion events trigger a review_workflow with disposition options: USE | QUARANTINE | DESTROY. Disposition decisions are recorded as immutable disposition_events rows linked to the excursion.

Recall management uses a recall_orders table initiating downstream propagation: the system identifies all serial numbers associated with the recalled batch, generates recall_notification records for each trading partner who received those units, and tracks acknowledgement through a typed status machine (ISSUED / NOTIFIED / ACKNOWLEDGED / COMPLETED).

Pharmacy Dispensing, e-Prescribing, and Patient Safety Systems

Pharmacy dispensing systems must enforce medication safety rules at the data layer: drug interaction checks, dose range validation, and allergy screening cannot be left to UI validation alone.

The prescription model uses a prescriptions table with a typed lifecycle: RECEIVED / VERIFIED / DISPENSING / DISPENSED | CANCELLED | ON_HOLD. Each state transition writes a prescription_events row. Dispensing records are separate from prescription records: a dispenses table captures quantity_dispensed, batch_number, expiry_date, dispenser_id, and dispensed_at. Multiple partial dispenses against a single prescription are fully supported through a running dispensed_quantity computed from the dispenses ledger, never stored as a mutable counter.

Clinical decision support rules are stored in a clinical_rules table with rule_type (INTERACTION | ALLERGY | DOSE_RANGE | DUPLICATE_THERAPY | CONTRAINDICATION), severity (CRITICAL | MAJOR | MODERATE | MINOR), and rule logic parameters. Rules are evaluated at prescription verification time; the evaluation is recorded in a clinical_alerts table with patient_id, prescription_id, rule_id, severity, alert_text, and outcome (BLOCKED | OVERRIDDEN | ACCEPTED). Override decisions require a justification_text and overriding_clinician_id: this record is immutable and forms part of the patient safety audit trail.

e-Prescribing uses an outbound_prescription_messages table for Surescripts, NHSEPS, or HL7 FHIR messages. Each outbound message records the payload, correlation_id, transmission_timestamp, and acknowledgement. Inbound prescription messages write to an inbound_prescription_queue processed by an idempotent consumer that deduplicates on correlation_id before writing to the prescriptions table. Delivery failures write to a transmission_failures table that drives retry with exponential backoff.

Controlled substance dispensing adds a national schedule enforcement layer: every dispensing event for a scheduled substance creates a mandatory PDMP report record. Outstanding reports are tracked until PDMP acknowledgement is received. Controlled_substance_dispenses are never deletable; voiding uses a VOIDED status transition with mandatory reason and authorising clinician.