State-of-the-art AI + data science
Save time with in-silico approaches
More than 10 years experienced data science team
Expediting Drug Development with Data-Driven Science
Embrace the future of drug product development with Leukocare. Our experienced team harnesses the power of advanced data-science to expedite and de-risk your drug development. Data science can shorten your development timeline by gaining more insights per wet lab experiment, significantly enhancing drug substance understanding and delivering more comprehensive conclusions, even with limited drug substance availability.
Leukocare’s Data Science Expertise
Leveraging cutting edge in-silico analysis, we can speed up and de-risk your drug product development. Our advanced approach goes beyond conventional methods, significantly enhancing drug substance understanding and delivering more comprehensive conclusions, even with limited drug substance availability. At Leukocare we’re not just advancing drug development; we’re pioneering a smarter, more efficient path to success.
Leukocare’s superior expertise in data science, encompassing our proprietary AI, machine learning, cutting-edge bioinformatics, and advanced biostatistics, helps you:
Identify degradation pathways, and key molecular features to get a more comprehensive product understanding and choose the optimal drug candidate(s) through Molecular Modeling
Rational selection from a wider set of potential excipients and immediately gain knowledge without human bias using our propriatery machine learning software “ExPreSo”
Speed up and improve your drug product formulation by exploring the broadest design space using our next-gen design of experiment (DoE) in combination with our proprietary database for selecting the best regulatory-approved excipients.
Determine the best formulations by harnessing the power of Response Surface Methodology (RSM) modeling to predict in-silico the most stabilizing excipient and buffer combinations.
Save time and increase accuracy in predicting long-term shelf life through Kinetic Modeling or Linear/Multiple Regression Analysis
Case Study
The right data enables the right decisions:
Design of Experiments (DoE) instead of high-throughput screening
Pharma 4.0: advanced device digitalization and data processing
Automated data quality checks in accordance with Quality by Design principles
AI to predict drug and formulation properties
Response Surface Methodology:
Contours based on RSM intuitively predict the best concentration of excipients' combinations
Synergistic interactions between excipients are detected and exploited for enhancing formulation composition.
Enhanced Drug Substance stability for the development of optimal formulations
Data-driven bioinformatics using innovative methods to save time in drug formulation development.
Case Study
Prediction of the biological effects and behaviors of molecules.
Molecular Modeling of the 3D protein structure of an antibody highlighting various liability sites.
Case Study
Data science for expedited excipient selection
State-of-the-art data science speeds up the formulation development process of Ad5 in liquid formulation
DoE-based formulation development allowed us to explore the full design space of eight excipients with only 40 formulations and to apply regression analysis to statistically estimate the stabilizing effects of excipients. Accelerated aging at high temperature (37 °C, left) showed predictive power in detecting statistically significant (p< 0.01) stabilizing effects of methionine on the titer.
Reinauer et al. (2020), J Pharm Sci
Facilitate more informed decisions to save time and reduce risk
Molecular Modeling
Molecular Characterization
Identification of degradation pathways
Stability assessment
Formulation development
Data science-based excipient selection
DoE to screen stabilizing effects and optimize excipients’ concentrations
Response Surface Methodology (RSM) modeling to predict the most stabilizing combinations
Long-term shelf-life prediction
Linear regression
Multiple regression
Kinetic Modeling
