Biotech

We offer various data analytics solutions for biotech firms, focusing on product development, manufacturing processes, and analytical methods development to drive innovation, enhance productivity, and accelerate time to market at a lesser cost.


Process Development

  • Optimise upstream, downstream, and drug product formulation processes with Design of Experiments (DoE).
  • Develop statistical models for product titer, quality attributes, purity, and excipient selection.
  • Utilise Multivariate Data Analysis (MVDA) for real-time monitoring, design space exploration, root cause analysis, and process robustness.
  • Analyse historical process data to see the consistency and trends across the scales.
  • Implement Quality by Design (QbD) principles based on the critical quality attributes (CQAs).

Process Characterization

  • Conduct risk assessment based on historical data to identify potential critical process parameters (pCPPs).
  • Develop and qualify scale-down models by Equivalence test and MVDA.
  • Define the experimental factors with an appropriate ranges and design space. Find the sweet spot in a design where performance is minimally sensitive to variation for all critical quality attributes (CQA) in the process.
  • Establish Proven Acceptable Ranges (PAR) and implement control strategies.

Analytical Development

  • Develop analytical methods using Design of Experiments (DoE).
  • Perform hypothesis testing for robust analysis. Analyse precision, accuracy, linearity, bias, and reproducibility with fitting curves and model comparing.
  • Perform product-to-product comparison and determine Quality Target Product Profile (QTPP).
  • Establish shelf-life and storage conditions with the confidence interval limits and crossing times calculation.
  • Biosimilarity assessment using Regression Analysis, ANOVA (Analysis of Variance), and Hypothesis Testing.

Manufacturing

  • Monitor process behaviour and performance with Statistical Process Control (SPC) charts.
  • Use the MVDA for fault detection and early variation detection.
  • Reduce the downtime with capability analysis.
  • Analyse historical data to see the comparison across scale.
  • Conduct the Annual Process Quality Review (APQR).

Process Modelling and Simulation

Mechanistic Modelling

  • Predict and optimise cell culture kinetics and antibody formation using physics-based dynamic cellular models.
  • Identify and eliminate process bottlenecks for consistent quality and nutrient fortification using timely dynamic predictions.
  • Simulate and analyse cell populations in a bioreactor using transient population balance models.
  • Maximise metabolic efficiency for fermentation processes using Metabolic Flux Analysis tools.

CFD-based Bioreactor Modelling

  • Simulate extracellular fluid environment to regulate mixing and mass transfer limitations at large-scale systems using CFD models.
  • Ensure equipment fit for the existing facility and processes via faster equipment characterisation and novel design feature evaluations.
  • Simulate and optimise parameters such as agitation, aeration, and nutrient distribution to ensure consistent scale-up from lab to production..
  • Automate process transferability to disparate bioreactors by CFD-aided operational space identification.

Data-driven and AI-aided Process Modelling

  • Unlock the full potential of the biomanufacturing processes by leveraging the state of the art statistical models.
  • Identify hidden patterns among multi parameters interactions using advanced AI models.
  • Reduce experimental analysis of metabolic intermediates by empirically finetuning the cell culture process.
  • Apply transfer learning approaches across existing product portfolios for product life cycle management.

Hybrid Modelling

  • Accelerate bioprocess development by leveraging the accuracy of mechanistic models with the adaptability of data-driven models.
  • UsPredict and optimise Critical Quality Attributes (CQAs) and process performance using Hybrid models.
  • Intensify the cell culture process even with a limited experimental data set guided by Hybrid models without calibration issues.

Stochastic Modelling

  • Predict the likelihood of a clinical trial's success by simulating variability in patient response, dropout rates, and adverse effects using Monte Carlo simulation.
  • Simulate the impact of variability in raw materials on the performance and stability of drug formulations.
  • Simulate long-term stability data for biopharmaceutical products to predict shelf life under different storage conditions using Morkov models.
  • Maximise metabolic efficiency for fermentation processes using Metabolic Flux Analysis tools.

Advanced Model Predictive Control

  • Improve the stability and efficiency of continuous processes by dynamically adjusting inputs like temperature and flow rates using MPC operators.
  • Use MPC to automate routine operations like feeding, glucose adjustments and cell bleeding.
  • Better quality and yield control using convoluted MPC embedding in the PID controllers and SCADA systems.

Tell Us About Your Project

Our team of experienced professionals at Graphtal is ready to transform your project from idea to
reality, ensuring alignment with your organisation goals through advanced data analytics and
predictive modelling.


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