SubsidieMeestersImplementation of new machine learning algorithms for the optimisation of drug formulations
MACHINE-DRUG aims to leverage machine learning to accelerate the prediction of crystalline forms in pharmaceuticals, enhancing drug efficacy and stability while exploring broader industrial applications.
Projectdetails
Introduction
Correctly developing and predicting crystalline forms with specific physico-chemical properties is essential to the pharmaceutical industry. The main challenge this industry faces is the fact that most active pharmaceutical ingredients in most drugs can interconvert into a different (usually more stable) polymorph, potentially reducing the solubility of the drug, slowing down the release of the API, and affecting the pharmacokinetics, bioavailability, and efficacy of the drug.
Challenges in Polymorphism
For instance, due to the complex interplay between thermodynamics and kinetics, it often happens that unexpected polymorphs emerge either in development (best case scenario) or long after the drug has been approved for market (worst case scenario).
- A previously known stable form that disappears.
- The sudden appearance of an even more stable form.
Both scenarios can have grave consequences. The new form may have new properties that are not suitable for the intended purpose of the drug, leading to significant economic and public health repercussions.
Project Goals
This ERC Proof of Concept project aims to implement new machine learning approaches that would allow accelerating the process of predicting crystal structures by a factor of 100. This advancement would make the process sustainable and enable the industry to investigate other crystal structures of the same drug to find the most suitable formulation (e.g., hydrates, salts, co-crystals, etc.).
Broader Implications
Beyond pharma (which is our target application for MACHINE-DRUG), polymorphism of chemical structures has significant importance across many other different industries. For instance:
- The polymorphism of a pigment can generate a different colour.
- The polymorphism of a chemical structure can lead to a material with significantly different properties (thermal, plastic, etc.).
As such, MACHINE-DRUG is a lean, targeted project with a clear scope, but its potential applications are limitless.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 150.000 |
| Totale projectbegroting | € 150.000 |
Tijdlijn
| Startdatum | 1-10-2023 |
| Einddatum | 31-3-2025 |
| Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- UNIVERSITE DU LUXEMBOURGpenvoerder
Land(en)
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Membrane Micro-Compartments
The project aims to develop a system for in situ structural analysis of membrane proteins to enhance drug interaction studies and facilitate their commercialization in the pharmaceutical industry.
Crystals of single chirality via non-equilibrium routes
This project aims to develop a novel method for converting racemic compounds into desired enantiomers by manipulating crystal stability under non-equilibrium conditions, impacting pharmaceutical production.
Automated, miniaturized and accelerated drug discovery: AMADEUS
AMADEUS is an automated platform for rapid, sustainable drug discovery that synthesizes thousands of small molecules daily, optimizing processes through AI to reduce costs and enhance accessibility.
ab initio PRediction Of MaterIal SynthEsis
Develop a predictive framework using first-principles simulations to assess the synthesizability of novel materials, enhancing materials discovery and design efficiency.
Machine Learning and Mass Spectrometry for Structural Elucidation of Novel Toxic Chemicals
LearningStructurE aims to enhance the discovery of novel toxic chemical structures by integrating chromatography, mass spectrometry, and machine learning to explore unknown chemical spaces in environmental samples.
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BioSim M2M: Molecules to MedicineBioSimulytics' BioSim M2M technology accelerates pharmaceutical R&D by predicting stable crystal structures and binding poses, reducing analysis time from 3 months to 3 weeks. | EIC Accelerator | € 2.499.525 | 2023 | Details |
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The ProM platform: New ways to drug the undruggablePROSION's ProM-platform aims to unlock and target the undruggable 85% of the human proteome, developing new therapies for hard-to-treat diseases like cancer. | EIC Accelerator | € 2.461.375 | 2022 | Details |
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Drug Discovery Intelligence
Het project ontwikkelt een AI-gestuurde softwareapplicatie om risico's in de medicijnontwikkeling te verminderen door het voorspellen van therapeutische targets en drug-target interacties.
BioSim M2M: Molecules to Medicine
BioSimulytics' BioSim M2M technology accelerates pharmaceutical R&D by predicting stable crystal structures and binding poses, reducing analysis time from 3 months to 3 weeks.
Personalised Adaptive Medicine
The PERAMEDIC project aims to develop a desktop-sized system for personalized polypill formulation using 3D printing and precise dosing to enhance treatment outcomes and patient adherence.
The ProM platform: New ways to drug the undruggable
PROSION's ProM-platform aims to unlock and target the undruggable 85% of the human proteome, developing new therapies for hard-to-treat diseases like cancer.
Universal GPCR Activity Sensor for Next Generation Drug Discovery
This project aims to develop a novel single-assay technology platform for GPCR drug discovery, enhancing detection and classification of drug candidates to improve efficacy and reduce failures.