Quality by Design methodology with DOE-driven design space definition meeting ICH Q8/Q9/Q10 guidelines.
Process design, qualification, and verification aligned with FDA and EMA process validation guidance.
Development cost savings through simulation-led design and statistically efficient experimental campaigns.
Pharmaceutical
Research & Development
Pharmaceutical R&D demands GMP-aligned methodology, Quality by Design (QbD) principles, and validated process data from lab through commercial scale. EnerTherm Engineering delivers the statistical rigour, simulation capability, and prototyping expertise to support drug product and process development.
R&D Challenges in
Pharmaceuticals
Regulatory complexity, long development timelines, and high failure costs drive pharmaceutical R&D methodology requirements.
Design Space Definition
ICH Q8 requires proven acceptable ranges for critical process parameters. DOE provides the statistical evidence to define robust design spaces that satisfy regulatory expectations.
Process Scale-Up
Transitioning from lab to pilot to commercial scale introduces heat transfer, mixing, and mass transfer differences. Simulation bridges the gap and de-risks scale-up decisions.
Equipment Qualification
New thermal equipment (lyophilisers, reactors, dryers) requires rigorous design qualification backed by simulation data and physical validation protocols.
Data Integrity & Documentation
GMP requires complete, attributable, and contemporaneous records. All R&D data must be audit-ready and traceable from raw measurement to final conclusion.
Our 6-Step
R&D Framework
A GMP-aligned R&D methodology from discovery through experimental design, simulation, prototyping, and validated scale-up.
Discovery & Scoping
Collaborate with stakeholders to define research objectives, success criteria, and the technical boundaries of the investigation.
Through structured workshops and technical reviews, we translate business challenges into research questions with quantifiable targets. We identify existing knowledge gaps, review prior art and published literature, and define the experimental or analytical approach that will yield actionable answers within budget and timeline constraints.
Experimental Design
Plan rigorous experiments using statistical methods to maximise information gained per test run while minimising cost and time.
We apply Design of Experiments (DOE) methodology — factorial, fractional factorial, and response surface designs — to systematically explore the design space. Each experiment is planned with randomisation, replication, and blocking strategies that ensure statistically valid conclusions and protect against systematic bias.
Simulation & Modelling
Deploy CFD, FEA, and thermodynamic models to predict performance and narrow the experimental matrix before physical testing.
High-fidelity simulations sweep critical process variables — temperature, pressure, flow rate, geometry — to identify the most promising design candidates. Virtual prototyping typically reduces physical test cycles by 40-60%, saving significant material and time costs while accelerating the path to a validated solution.
Prototyping & Testing
Build and test physical prototypes under controlled conditions, benchmarking real-world results against simulation predictions.
Each prototype undergoes structured test protocols measuring thermal performance, mechanical integrity, and process efficiency across a matrix of operating conditions. Deviations between simulated and measured performance are systematically analysed, feeding directly into targeted design refinements.
Analysis & Optimisation
Consolidate simulation, sensor data, and test observations to validate assumptions and optimise the final design.
Statistical analysis, AI/ML pattern recognition, and response surface methodology are applied to extract actionable insights from the data. Multi-objective optimisation balances competing performance targets — cost vs. efficiency, weight vs. durability — to converge on the best overall solution.
Validation & Scale-Up
Confirm results through confirmation runs, produce production-ready documentation, and support the transition from R&D to industrial deployment.
Final validation ensures performance meets all agreed criteria under realistic operating conditions. We deliver complete technical packages — process specifications, CAD models, control guidelines, and risk assessments — so the solution transitions seamlessly from laboratory or pilot scale to full production.
What You
Receive
GMP-ready R&D deliverables supporting regulatory submissions and process validation.
Feasibility & Risk Assessment
Technical viability evaluation with preliminary risk assessment (ICH Q9), financial projections, and recommendation for development pathway.
DOE Design Space Study
Statistical definition of proven acceptable ranges for critical process parameters, supporting ICH Q8 design space submissions.
Process Simulation Model
Validated thermal and fluid dynamic models for reactors, dryers, or lyophilisers, predicting performance across scale and operating scenarios.
Pilot-Scale Validation Data
Structured test campaigns at pilot scale with IQ/OQ-aligned protocols, deviation reporting, and statistical process capability analysis.
Equipment Design Specification
User Requirements Specification (URS) and Functional Design Specification (FDS) derived from validated R&D data for equipment procurement.
Technology Transfer Package
Complete documentation supporting technology transfer from R&D to manufacturing, including process parameters, control strategy, and validation master plan inputs.
Proven Results in
Pharmaceuticals
Based on R&D projects across API processing, formulation development, and lyophilisation cycle optimisation.
Pharmaceutical
R&D FAQ
Common questions about research and development for pharmaceutical applications.
DOE is the core statistical tool for QbD. It systematically identifies critical process parameters, quantifies their effects and interactions, and defines the design space — the proven acceptable range of parameters where product quality is assured, as required by ICH Q8.
Yes — process simulation models validated against experimental data provide supporting evidence for design space justification, scale-up rationale, and control strategy documentation in regulatory filings to FDA and EMA.
While early-stage R&D may not require full GMP, we apply GMP principles (data integrity, traceability, deviation management) throughout. As projects progress toward clinical or commercial scale, full GMP documentation practices are implemented.
We support R&D across API reaction optimisation, crystallisation, drying (spray, fluid bed, lyophilisation), granulation, coating, and heat-sensitive formulation processing. Any thermal or process engineering challenge in pharma manufacturing is within our scope.
Ready to
Develop?
Our pharmaceutical R&D team delivers QbD-aligned DOE, process simulation, and validated prototyping for drug product and process development.
- Integrated DOE, feasibility, and prototyping
- Pharmaceutical-specific R&D methodology
- Simulation-led development with physical validation