Technologies in drug discovery and development
The process of launching a new drug generally comprises two major stages: drug discovery in the preclinical phase and drug development in the clinical phase. In the preclinical phase, suitable molecular candidates are selected from a large number of possible compounds. Multiple steps and cycles of testing are required to study the compound-target interactions in the clinical phase.
The process of drug discovery and development is costly and time consuming. According to a 2014 study by the Tufts Center for the Study of Drug Development (TCSDD), the cost of developing a new drug, from research and development to marketing approval, is approximately $2.6 billion (Scientific American, 2014). Clinical development contributes to almost 60 % of the total cost as well as the majority of the discovery cycle time (Peng et al., 2017). Therefore, improving the probability of success of drug candidates in the clinical development presents one of the greatest challenges and opportunities for pharmaceutical research.
In fact, Chemical & Engineering News estimate that only 13.8 % of compounds make it through the clinical trials to approval. Out of all drugs being developed, cancer drugs have the lowest success rate (Chemical & Engineering News, 2018). This has lead big pharma to invest in technologies that can identify issues that might cause a drug to fail early in the drug development process, in many cases before the compound goes to clinical testing (Wilson, 2016).
Overview of key technologies in drug discovery
Technologies are of key importance in both drug discovery and development. Below you can find an overview of some of the most influential technologies in the pharmacological research which have improved the efficiency of the process of drug discovery and development.
High-throughput screening enables researchers to screen huge chemical libraries against an ever-increasing range of targets for lead discovery. By allowing scientists to quickly conduct millions of chemical, genetic, or pharmacological tests, HTS has greatly contributed to the early-stage drug discovery. Concentrated efforts in automation, miniaturization and the necessary readout technologies have enabled the growth of HTS, allowing us to screen compounds far more rapidly and effectively than predicted even a few years ago (Rudd, 2017).
3D biological printing
Bioprinting is synchronous positioning of biomaterials and living cells in a layer-by-layer stacking organization to fabricate 3D constructs (Weijie et al., 2017). Currently, most of the in-vitro (‘in the glass’) drug discovery assays are performed in 2D cell cultures which do not simulate the exact in-vivo conditions, making 2D models largely inaccurate. 3D tissue models therefore provide better results and greater in-vitro / in-vivo correlation for drug screening compared to the traditional 2D models. For example, 3D printed versions of cancer tumours give researchers a better look at how tumours behave and a more accurate measure of how they respond to treatment. One of the great implications of 3D bioprinting is its potential role in replacement of pre-clinical animal testing (Peng et al., 2017).
Genome editing technologies
CRISPR-Cas, tool for editing genomes, is widely known for its therapeutic use, yet its applications might be equally important in drug discovery. By using the system to deliberately activate or inhibit genes, researchers can determine the genes and proteins that cause or prevent disease, and therefore identify targets for potential drugs (Scott, 2017). Genome editing technologies enable scientists to more accurately verify the safety and efficacy of drugs which ensures that such models are better predictors of what will happen in clinical trials.
Next-generation sequencing (NGS) enabled genomes to be sequenced for less than $1000, giving even smaller laboratories the chance to understand many diseases at the genetic level (The European Laboratory Research & Innovation Group). It is especially important in the field of cancer research as cancers are mainly caused by gene mutations. Using NGS, an entire human genome can be sequenced within a single day and the use of NGS has already led to the discovery of a number of new disease genes. This technology has a great potential in individualization of treatment as well as decrease of cost of drug development (Torshizi & Wang, 2018).
Most large pharmaceutical companies use virtual screening operations to facilitate their drug discovery programs. It offers rapid and inexpensive identification of small molecules which bind to the intended target. The idea behind virtual screening is that a library of small compounds is attached to the binding site of a protein and based on the results, a fraction of compounds is selected and further tested.
Virtual screening differentiates between active and inactive compounds, thus reducing the number of molecules moving forward and possibly offering a complementary tool to high-throughput screening (Kontoyianni, 2017).
Potential of machine learning in drug discovery
Due to the emergence of new experimental techniques such as HTS, there has been a remarkable increase in the amount of biomedical data. Efficient mining of large-scale chemistry data has become a crucial problem for drug discovery. Large data volumes in combination with increased automation technology have promoted the use of machine learning (Chen et al., 2018).
Machine learning has been used to identify and screen potential drug candidates. This technology can train to analyse large sets of chemical and biological data to identify drug candidates with high success rates much faster than conventional methods (Wilson, 2016). For that reason, many pharmaceutical giants such as Merck have been partnering with AI-driven companies (Pharmaceutical Technology, 2018).
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