Apheris for Pharma

Collaborate with partners on data and AI to innovate on new medicines, diagnostics and evolve the practice of life sciences

The pharmaceutical industry is undergoing a huge transformation and has made tremendous efforts in recent years to harness the power of data. To realize the full potential of data and AI, research institutes, academia, and large organizations must now shift their focus from the inward to the outward, collaborate on sensitive data and share domain expertise.

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Our Customers & Partners

BCG logo
Technische Universität Darmstadt logo
Gaia X  logo
Apha  logo
COREVAS logo
Federal Ministry of Education and Research

“Apheris' innovative federated data platform allowed us to train robust predictive AI models with two of our partners while keeping all data private, onsite, and safe.”

Industry scientist
at top 20 pharma

Challenges

Current challenges in collaborative research and data sharing in the pharma industry are manifold: Valuable data is siloed and inaccessible, incomplete, or non-standardized. Regulatory requirements are ever-changing, and technical complexity makes collaboration unfeasible.

Why Apheris

We enable pioneers to create sustainable data ecosystems that are built on trust, ethics, and deep collaboration. Together with their partners, they can identify new drug candidates, de-risk and accelerate clinical trials and build new diagnostic solutions that improve patient outcomes.

Solution

Breakthrough innovation in health ecosystems requires more than just sharing data. Apheris allows deep and trusted collaboration during all stages of the data science workflow and beyond. Starting from identifying the most valuable use cases, to standardizing and leveraging FAIR data, establishing interoperability between tech stacks, the training of robust machine learning models to the full governability and reproducibility of data and AI models between all collaborating organizations.

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Federated Learning

Deep access to granular data and insights, without having to move and centralize data. Federated Learning results in increased machine learning performance, while respecting data ownership and privacy.

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Data-centric AI

Machine learning models are only as good as the data they are being trained on. Apheris enables a data-centric view on ML innovation and supports the entire data science workflow – from data acquisition to model operationalization.

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Research-grade Data

Apheris helps to curate and standardize available and future data sets to ensure interoperability and enables access to longitudinal data that is complete, standardized, and prospective.

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Privacy-enhancing Technologies

Besides a federated architecture, Apheris leverages different PETs with the highest level of precision to establish governance, privacy, and security, as well as to maintain data quality and usability.

Collaborative Data Ecosystems in Pharma

A collaborative data ecosystem is an alignment of business goals, data and technology, among two or more participants, to collectively create more value than each can create individually.

Potential shared value propositions in pharma:

  • Expand data applications across therapeutic areas
  • Accelerate drug discovery processes
  • Enable precision medicine
  • Build marketplaces based on federated health data

Resources

White Paper

Federated Learning on Vertically Distributed Healthcare Data

If organizations with complimentary data sets could join their databases together, it would become possible to train strong machine learning models that could outcompete current industry standards. However, data sets are protected due to their inherent value. They also often contain intellectual property, which cannot be disclosed or shared with other parties. Furthermore, medical data can contain sensitive and personal identifiable information, which is governed by regulatory frameworks that often prevent companies from easily sharing data within their professional networks.

White Paper

Privacy-preserving Data Ecosystems in Support of Drug Discovery

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Although machine learning in its various forms has been deployed for drug discovery and development for several decades, the era of Big Data has created a niche for Deep Learning. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

White Paper

Privacy-preserving Data Science on QSAR models

This case study demonstrates the utility and effectiveness of Apheris’ platform and services for data science on not directly accessible and distributed data. An integral part of our platform is the implementation of federated learning, which decentralizes the learning algorithms to access a number of different data sets while maintaining privacy. This process eliminates the need to pool sensitive data, which has been a major hurdle in practice due to a lack of trust between different data providers.