It seems that ‘one drug for all’ may become rarer, mainly because future interventions will be context appropriate in terms of disease subclassification, patient genetics and treatment history, drug pharmacokinetics, pharmacodynamics, etc. Disease variants and their stages of progression are being defined at ever higher resolution.
Point-of-care companion diagnostic kits and sophisticated drug vectorization technologies are becoming more common.
Engineers and life scientists are working together to develop new sensors to monitor ever more closely changes in biomarker status in disease and under treatment. An increasing number of therapies are targeted to specific patient profiles in order to achieve increased effectiveness and improved tolerance.
These considerations will have major effects on how we plan all aspects of drug discovery, development, fabrication and usage.
As we become more connected, we increasingly rely on big data collected from large populations. The approaches of pharmacoepidemiology and pharmacovigilance evaluate treatment outcomes using real world evidence and observational studies of real practice. Health technology assessment evaluates drug efficiency, i.e. its risk-benefit ratio in the context of prevalent health care conditions and therapeutic alternatives available locally. These areas of expertise not only influence pharmaceutical discoveries but also support the effective implementation of therapeutic innovations in a context-appropriate manner in various communities around the world. This is a key issue in translating pharmaceutical discoveries in less affluent countries, especially, for instance, with the emergence
of complex biologic drugs. Such a global reach at the population level will become increasingly important in the future, considering the threats of new disease outbreaks on health, economies, and communities in all countries. Combined with better approaches to vaccines development and production, these approaches should support a rapid and specific global response to pandemics.
Creating value using big data depends on our ability to process the information to generate knowledge. The emerging field of applied artificial intelligence has the potential to revolutionize pharmaceutical sciences, thanks to its ability to detect patterns in large data sets and learn from experience. In target identification, it may help optimize multi-target
approaches against cancer through better analysis of signalling networks. In drug discovery, the analysis of structure activity relationships may benefit following high throughput screening of large chemical compound libraries.
To a significant extent, artificial intelligence applications will also impact diagnostics, therapeutic decision making and pharmacy practice. Soon, artificial intelligence may provide a reliable, low cost assistance to clinicians. It may even train them under certain circumstances, or replace them in areas where they are not readily accessible. By 2020, it will be high time to reflect on the impact of artificial intelligence on pharmaceutical sciences, pharmacy practice and health outcomes worldwide.