The race for the effective cure is expediting the innovation in drug discovery technology.

Providing data-driven, personalized cure to patients is a significant expense for the pharmaceutical industry. Over the last decades, high throughput screening (HTS) has been the widespread method for early-stage drug discovery, allowing automated testing of large numbers of chemical and biological compounds for a specific biological target. While these past decades have witnessed major innovations in this space, current breakthroughs in drug discovery technologies accelerate the speed of drug design and discovery.

Undeniably, the enthusiasm for AI has been growing at a faster rate within the pharmaceutical sector. The technology has already shown its potential in diverse industries, and in pharmaceuticals, it has an incredible impact on drastically lessening the amount of time required to develop a new drug. While it is able to handle massive amounts of data from various datasets, AI can be used in target identification, forecasting, patient matching, automating molecule design and more.

The use of robotics by advanced laboratories is also surging and speeding up the processes of drug discovery. Griffith Institute for Drug Discovery (GRIDD), Griffith University, for instance, uses HTS robotics for both phenotypic screening and target-based screening.

The promise of such technologies certainly supports in analyzing vast amounts of data and can be crucial facets of the drug development pathway.

Key Trends in Drug Discovery Technology Innovations

The drug discovery process has always been dynamic and evolving thanks to unprecedented technological innovations, new disease discovery, transition in disease prevalence and global regulatory frameworks. Technological innovations such as AI and machine learning for drug discovery and early development can have a considerable impact on the creation of safer and more effective drugs by helping in data-driven decision-making around which potential compounds are needed for further test and development.

Enter Fragment-Based Screening

Fragment-based screening (FBS) over the last decade has become widespread and proven to be a viable alternative to HTS in drug discovery. The requirement of this screening model has largely been empowered because Fragment Space is relatively smaller than Chemical Space and can be more effectively investigated with a small library. Creative Biolabs, a custom biotechnology and pharmaceutical services prodder, created well-established binding assays for fragment-based hit identification. With sensitive label-free detection and the ability to spot weak interactions for small compounds, less than 250 Da, the company can provide selective data for hit identification.

Innovation in Clinical Trial Models

Recent years' innovations have substantially improved trail efficiency. Most importantly, innovations such as adaptive, patient-centric, precision medicine and real-world data trials, have revolutionized the way drugs being launched. Indeed, data shows that drugs developed using these innovations saw a 10-21 percentage point increase in phase II and III likelihood of launch compared to drugs developed with traditional methods.

Although drug discovery is a complex process and constant flux, recent innovations in technology offer significant opportunities for a more patient-centered approach. There are still rooms for innovative products and drug discovery projects using such promising technologies that will enable pharmaceutical companies to deliver precision medication.