Generally speaking, no. The clinical trial sponsor is still responsible for medical care of the patients during the study. The costs of ancillary care to the patients (as opposed to the cost of the core treatment) is typically a minority share of the total cost of a clinical study, so even a 50% savings might be a 5% reduction in the total study cost.

There can be some cost savings to conducting studies in Eastern European countries. But again, that’s less to do with single-payer healthcare and more because the going rates for physician stipends, site costs, and CRA salaries are lower.

To answer your question, the pharmaceutical companies pursue both In-house or external R&D. Bigger companies are especially doing more R&D outside to reduce risks/costs and focus their efforts/budgets on only sure bets.

Following factors affect the decision to perform research in-house/externally:

1. Capacity & Capabilities
2. Therapeutic area focus
3. Risk Appetite & budgets

There are different phases of research that can be done outside as well as different models of collaborations.

Yes it is, to help all of the Native Americans. All minorities need to do their part in clinical trials so that future medicines have data to represent their tolerances and outcomes as well. Generally it’s a white working male that participates. Each race react differently to medicines, so for better medical treatment, participation in development is crucial.

Over the past 10 years we have seen an increasing amount of complexity in machine learning algorithms that are being applied to everyday uses, from internet searches to voice recognition and self-driving cars. Pharmaceutical research and development (R&D) has also seen a number of artificially intelligent and machine learning developments take hold in the industry. Applying these methods to mine the growing amount of big data that is constantly being generated from all of the clinical trials taking way, not only enables us to learn from past data, but also to predict a molecule’s properties and efficacy in the future.

One of the latest machine learning algorithms that is making attention in the headlines is the deep learning (DL) platform, which consists of artificial neural networks (ANNs) with multiple layers of data to access. Recent publications suggest that these advanced algorithms may have advantages over traditional machine learning methods and offers a slight edge in predictive performance. Machine learning methods, such as DL, will apply across a wider array of endpoints in the near future, making predictions for advanced synthesis techniques, ADME/Tox studies, and even clinical trials themselves. Studies are also showing many other potential applications of deep learning and AI well beyond cheminformatics.

Another surprising fact is that the same algorithms we already use everyday, such as the ones for smartphones, voice recognition, and so on, are the same algorithms that we should be considering for cheminformatics, and other areas in the pharmaceutical industry. Large companies, such as Google, Amazon and Facebook, have actually been using deep learning and AI for years. In the near future, once AI is fully integrated into the drug development process, we will be able to see the benefits in research and development and healthcare in general.