Before the advent of drug discovery using Artificial Intelligence, pharmacists built a foundation on the traditional methods. The process of drug development is a complex one, starting from drug discovery and research all the way to manufacturing, marketing, and sales. It has substantial capital requirements and many associated risks.
One of the most significant risks associated with the drug discovery and pharma manufacturing process is the probability of drug approval for clinical trials. It usually takes a long time before its official release for public use.
Research holds that only about 12% of medicines discovered get approved for clinical trials. The cost involved with drug discovery can run into millions of dollars and could take as long as 15 years of relentless work.
The approval rate is low because drugs don’t just have to be efficacious. They need to have a relatively similar effect and be safe for all people while following all the laid down standards for drug discovery and development.
Table of Contents
Stages of the Drug Development Process
Drug Discovery
The key drivers for choosing therapeutic areas for drug discovery are the medical need for the same and the prevalence of a particular disease. Other factors to consider at this stage include:
- The research and development cost
- The drug’s marketability in the final production stage
- The current and potential competition
Given that the project under consideration meets all the criteria, it will be under consideration pending other ongoing drug development and research processes in the pharmaceutical company.
Preclinical Research
Drugs that are researched pass through laboratory and animal testing to satisfy fundamental safety questions. At this stage, researchers seek to find out the toxicity level and whether or not the drugs are potentially harmful.
Preclinical research is of two types: in-vitro (cell-based testing) and in-vivo (animal-based testing).
Clinical Research
After the preclinical research process is successful, the next stage is clinical research. Clinical research involves the actual testing of the drugs on humans to see their interaction. A particular drug that is declared safe for humans in the preclinical stage still needs to stand trial on human test subjects before it is made available to the general public.
FDA Drug Review
After the newly discovered drug has gone through the stages mentioned above it has to be government-approved for public consumption. For this, researchers have to satisfactorily answer essential questions on the safety and toxicity of the drug.
If the FDA eventually approves the drug discovery, it works with the researchers to ensure proper prescription and labeling. The FDA drug review process is long and could take between 6 months to a year before a drug is approved, given that the medicine meets the requirements.
After approving a new drug, what follows is the FDA post-market drug safety monitoring. The FDA writes down customer’s complaints about probed drugs, and the drug will undergo further clinical review and observation.
Read: How Data Analytics is Driving Value for Pharma Industry
Traditional Drug Discovery Process
Before venturing into the large-scale production of drugs, companies conduct a detailed market analysis to understand the degree of demand and the market’s existing competition. Below is an insight into the conventional drug discovery process:
- The first stage of traditional drug discovery is target identification and validation. Target identification finds a gene or protein that plays a significant role in the disease.
- The next step is to determine whether the chosen lead compound produces the expected effect on the identified target. There are different approaches to identifying these, including virtual screening and knowledge-based systems.
- Finally, the lead optimization stage is a vital part of the drug discovery stage. The lead compounds discovered are “synthesized and modified to improve potency and reduce side effects”.
Big Data and Artificial Intelligence to Optimize Drug Discovery
Artificial Intelligence in drug discovery has helped scientists bypass the usual long-term research. Because drug discovery involves a large body of data when it comes to choosing the lead candidate, data science can leverage this to provide accurate and effective solutions.
By training ML models with existing datasets, you can find out lead candidates much faster. In fact, automating the entire process with AI/ML will save a lot of time and resources.
With AI, scientists can easily monitor a drug’s progress, its complications, improvement in where and when necessary.
Methods That Leverage Artificial Intelligence in Drug Discovery
Deep neural network for drug discovery
A Deep Neural Network mimics many of the functions of the human brain. They comprise semiosis, weight, bases, neurons, and functions. In machine learning drug discovery, DNNs play a massive role in aiding drug indication prediction. It shows to what extent a drug will work or interact with the human body.
Image Analysis
Image analysis helps in the predictability of drug indications as well as bringing down the possibility of error. Determining the target molecules and zeroing down on a particular pathogenic disease is beneficial to research.
It helps analyze and show the interrelationship between proteins and reviews the groups that can effectively treat a particular disease.
Network analysis in drug research
ML is also aiding many computational biologists in using network analysis to unravel different data types of test relationships concerning drug discovery. For instance, you can understand states of disease and drug mechanisms through signaling and metabolic pathways. Various means of network analysis are also helpful within drug discovery.
Recently, some advanced research establishes a network showing the relationship between molecular mechanism, disease, and targeted genes. Furthermore, there have been efforts to foster joint efforts and novel research.
Single-cell analysis tools
The difficulty facing drug discovery is the constitutional, genetic and functional heterogeneity of healthy and disease tissues. This has led to the innovation of tools for single-cell transcriptomic, multiplex proteomic, and genomic analysis. Single-cell analysis tools are categorized based on the analytes developed to measure, i.e., multiplex proteomic, transcriptomic, and genomic or the combination of these.
Its advantages in drug discovery are in high throughput drug screening via single-cell phosphoproteomics. It helps in the detailed analysis of how off-target and on-target drug interactions influence protein levels and signaling networks.
Challenges Faced During AI Drug Discovery Processes
- AI in drug discovery faces challenge of retaining special skills such as medicinal chemistry.
- There is no public engagement and proper education on drug discovery issues for public and government policymakers. People do not know the benefits and risks of new medicines and regulatory systems.
- Skills such as clinical pharmacology, drug metabolism, and in vivo capabilities have lost relevance, and there is a challenge integrating them into machine learning.
- Return-on-investment isn’t specific except there is the market is sufficiently large.
Benefits of Artificial Intelligence in Drug Discovery
AI/ML today has so many benefits to manufacturing industries, especially pharmaceutical industries. Below are the benefits of AI drug discovery:
- AI drug discovery helps save time and provides more accurate results on big data analysis, thereby making research work way more straightforward in the pharmaceutical world.
- Also, AI drug discovery has helped in the risk of making errors that could limit the development process of a new drug to a manageable level. This has been possible because AI helps analyze progress and what is needed either addition or subtraction. For instance, if COVID-19 occurred when there was no Artificial Intelligence efforts for drug discovery; it would take many years before realizing a vaccine.
Takeaways
Despite the shortfalls of Artificial Intelligence in drug discovery, there is no denying that AI has come to stay in the pharmaceutical industry. It has been beneficial to drug development and research. It saves time, aids in getting accurate results, and eight predictability of drug interaction in the body before clinical testing. AI keeps evolving daily, and it can only get better as time goes by.
