For the chemical producing industries, compound toxicity is a main driver of massive costs and failure rates. Earlier screening for toxicity, without using animals, is a paradigm shift that is critical for efforts to reduce late-stage attrition. However, a predictive platform to enable earlier, standardized implementation of new and improved strategies to support safer chemical design, has been lacking – this is why AsedaSciences developed the 3RnD platform.
AsedaSciences’ 3RnD platform is a comprehensive, AI enabled data management solution that transforms the interaction between chemical structure and human cellular function, into actionable safety risk predictions to support R&D productivity improvements for the chemical producing industries.
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For Medicinal Chemists, understanding which chemical scaffolds represent the safest starting point at the hit-to-lead stage, is challenging. Similarly, for Drug Discovery teams, defining an early estimate of the potential Therapeutic Index (TI) as SAR modifications are made, within or across chemical cohorts, is difficult.
The 3RnD platform was designed to help solve these challenges by enabling the evaluation of toxicity predictions earlier in discovery to avoid costly failures downstream. This supports the selection of safer scaffolds at hit-to-lead and provides teams with an evolving view of the predicted TI throughout early lead optimization. This improves decision making around chemical strategy and guides stage gate progression, as it helps to determine the potential trade-offs to safety, dose and efficacy.
The agricultural chemical industry requires innovative new pesticides that overcome resistance and are safer for humans and the environment. However, characterizing the associated safety risk has become increasingly challenging, particularly with the implementation of 3Rs. As a result, earlier integration of new in vitro cell-based screening approaches into the R&D process is required. However, rigorous validation is usually lacking, making routine implementation a challenge.
AsedaSciences addresses this challenge by providing a validated platform to predict potential toxicity risk earlier. High content, high throughput phenotypic screens are combined with machine learning and cloud visualization to enable incorporation of high-quality safety predictions into a more modern risk assessment approach. R&D teams can now apply a logically guided, data driven triage cascade for improved compound selection, prioritization and progression, while simultaneously addressing the 3Rs requirements.
Due to environmental persistence, accumulation and sustained exposure of many industrial chemicals, understanding the associated health safety risks has become a priority for governments and consumers globally. This is driving industry efforts to develop green chemistry initiatives that replace older chemicals of concern with safer alternatives. However, 3Rs initiatives and cost considerations mean that the associated safety evaluations must use more modern in vitro screening approaches.
AsedaSciences addresses these needs by providing a predictive, in vitro platform that can support rapid, accurate and cost-effective toxicity assessments to help prioritize green chemistry initiatives. By enabling risk-based stratification of higher risk chemicals, industry can implement a modern risk assessment approach to both prioritize chemicals of concern, while supporting selection and prioritization of safer alternatives, using a platform that contributes to their 3Rs objectives.
The EU ban on animal testing for cosmetics, driven by consumer demand, has created product innovation challenges for the cosmetics industry. This has created a critical need for alternative non-mammalian and human cell-based phenotypic screening approaches to identify toxicity risk and ensure consumer safety. However, accuracy, validation and data translation for these New Approach Methodologies (NAMs) remains a significant challenge.
AsedaSciences is addressing this critical need by delivering validated high-content phenotypic screens (human cells and zebrafish) covering critical toxicity risk categories. Results are integrated with AI based analysis and visualization on our 3RnD platform, transforming the high-complexity data into actionable decisions. By integrating these well-validated NAMs with a data transformation solution, we can support the cosmetics industry in their drive towards renewed innovation, while delivering safe cosmetics to consumers.
Natural Products (NPs) are undergoing a ‘renaissance’ as an inspiration and source for new product solutions across multiple industry segments. This is being driven by technology advances in fields such as isolation, genomic mining, engineering and microbial culturing. The combination of these advances, historical product successes and substantial scaffold diversity is driving renewed interest in NP research. However, challenges due to structural complexity, biological promiscuity and associated toxicity risk, still remain.
To support initiatives in this field and to address the need to identify toxicity risk early, AsedaSciences has used our integrated phenotypic screening, predictive safety analysis and data visualization platform to help identify potential toxicity risk for a broad range of NPs. New NPs can be compared based on structure, or biological fingerprints, to a broad library of NPs housed in the 3RnD database. This valuable tool enables earlier deconvolution and prioritization of promising NP extracts and individual compound isolates.
Consumer demand for Nutraceutical products, containing additives or supplements that support actual or perceived health benefits, is growing rapidly. These additives often integrate plant extracts or natural products, where the safety risk from the combination, concentration and individual contribution of associated compounds may not be fully understood. This has led to published cases of toxicity due to the lack of pharmacological and toxicological studies employed to determine risk.
To address this issue, the combination of phenotypic screens and machine learning algorithms developed by AsedaSciences can support industry initiatives to rapidly and accurately predict potential toxicity risk for Nutraceutical additives. By proactively screening early in product development, compounds or mixtures integrated into Nutraceutical products can be assessed for potential safety liabilities. This allows industry to reduce product safety risk, that can impact consumer confidence, while still satisfying demand.
Excipients include preservatives, solubilizers, vehicles and colorants and are dominant components of drug, food and cosmetics formulations. While originally thought to be inert, recent publications demonstrate the potential for interaction with cellular targets, causing unwanted biological effects. However, little is known about the potential health risks associated with these commonly ingested compounds. While the majority of excipients are used without obvious biological effects, some have been de-listed by the FDA and others have been associated with various health related concerns.
AsedaSciences has published a proof of concept on the importance of integrating live-cell phenotypic screening with machine learning to identify potential risks associated with excipients. In a collaboration with Novartis, AsedaSciences identified a number of excipients causing cellular toxicity that correlated with other mechanistic screening approaches. The results demonstrated that excipients, as a class, should be treated with the same level of concern for safety assessment as the active pharmaceutical ingredients.
Food additives encompass chemical substances that are intentionally added to food to impart or improve flavor, aroma, color, shelf life, texture, nutritional value or cost. Nearly a third of consumers consider chemicals in their food as their top food safety concern, with environmental sustainability also an area of increasing importance. As a result, understanding the toxicity risk associated with food additives is a critical need for industry.
By combining high-content phenotypic screens with machine learning, AsedaSciences is addressing this need by providing a platform to deliver predicted toxicity risk that supports a modern risk assessment approach. Biological fingerprints generated for current and new food additives can be compared to our large training set of chemicals with known human safety profiles, providing an accurate prediction of potential safety risk based on similarity. In addition, AsedaSciences is building a library of annotated food additives for comparison.