A Swedish SME offers a unique computational methodology for virtual prediction of human clinical pharmacocinetics and metabolism
A Swedish pharmatech SME has developed a validated platform based on human clinical data for predictions of ADME/PK (Absorption, Distribution, Metabolism, Excretion, PharmacoKinetics) from chemical structure. It enables predictive analytics for pharmaceuticals, cosmetics and toxicology. The SME seeks partners for licensing for existing models with pharmaceuticals as well as technical or research cooperation agreements for new models or new application areas in Europe, Asia and in the US.
The SME is looking for partners for licensing agreements with pharmaceutical companies (for example, within the area of prodrugs, antibiotics and drug metabolites) for the models that are already in the platform. The SME is also interested in partners on the basis of technical or research agreements to develop and validate new models and/or explore new application areas. Current clients are primarily in pharmaceuticals and new applicaiton areas could include partners in chemical industries inthe area of toxicology or partners in cosmetics to explore benefits of the platform to predict uptake of and exposure to cosmetics.
A Swedish SME, active in the area of pharmaceutical technology, has developed a unique platform, including software, for high quality predictions of human clinical ADME/PK (Absorption, Distribution, Metabolism, Excretion, PharmacoKinetics) of drugs and drug candidates directly from chemical structure. This computational (in-silico) methodology, based on unique algorithms and database, machine learning, artificial intelligence (AI) and conformal prediction methodology, has been internally and externally validated and shown to outperform conventional in vitro and animal models in accuracy and range. It is also applicable for drug metabolites and various kinds of chemicals. Lab methods, such as different human cell systems (in vitro) and animal models, are commonly used for producing prediction of doses and exposures of drug candidates in humans. Good predictions of ADME/PK in humans are essential for setting safe start doses in early clinical trials and for assuring adequate exposure and pharmacological profiles in patients. Prediction methods with poor quality jeopardize successful and cost-efficient drug discovery and development. Animal-based ADME/PK-methods have been demonstrated to have lower median prediction errors and to cover a significantly broader range of compounds compared to in vitro methods. However, some essential parameters are poorly predicted from animal data and maximum errors are extreme for both animal and in vitro based predictions. In silico tools have been produced by many, often based on in vitro data. These have not reached a prediction accuracy and range as good as for lab methods. The platform utilizes unique human clinical data (quality-checked and stratified) and algorithms, machine learning, AI and conformal prediction methodology (which gives guaranteed confidence estimates). The predictions outperform lab methods in accuracy and range. The platform, which has more than 50 models, including for uptake from the skin, eyes, lungs and blood-brain barrier, has been extensively validated internally and externally (also by major international pharmaceutical companies). Recently, the SME also launched new software – a human clinical ADME/PK-studio - with features that enable direct optimization of characteristics of candidate drugs and drugs in a user friendly mode and at an advantageous price. The SME is looking for partners for licensing agreements with pharmaceutical companies (for example, within the area of prodrugs, antibiotics and drug metabolites) and partners on the basis of technical or research agreement for developing of new models and/or new application areas for example relating to toxicology or cosmetics development.
Advantages and innovations
The SME has developed and validated a unique, confidence-assuring, computational methodology that outperforms comparable lab methods - see attachment for an overview. Advantages include superior accuracy (lower errors), range and compound coverage, completeness and successful external blind validations. The platform outperforms laboratories and other computational methods, also according to external blind validations by major international pharmaceutical companies. Compared to traditional methods it enables drug developing companies and institutions to: - significantly reduce or replace animal and in vitro experiments, - frontload decision-making, - improve cost-efficiency and productivity, and - reduce substance synthesis and risks, without losing predictive quality.
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