The pharmaceutical industry has been one of the most favored sectors by the implementation of emerging technologies such as the digitization of manual tasks to save time and effort. But the most important change has come from Artificial Intelligence (AI), which has allowed many improvements, specifically in speeding up drug discovery and development, as well as reducing research costs and the percentage of failures in clinical trials.
Creating a drug requires the synthesis of a compound that can bind to a disease-causing target molecule and modulate it. To find the right compound, researchers screen thousands of candidate targets, and once one is identified, they screen huge libraries of similar compounds for the optimal interaction with the disease protein. Currently, it takes more than a decade and hundreds of millions of dollars to get to this point. However, AI makes it possible to simplify the process and reduce the time and money needed to launch these new drugs.
This case is just one of many other uses of AI in the pharmaceutical sector. In this article we will discuss some of the most significant ones and the impact it is having on the entire value chain.
Discovering and developing new drugs
The process includes identifying the therapeutic target causing the disease, which is where the drug will act to achieve the cure of the disease, through the design that allows us to know in advance what characteristics the candidate drug molecules must meet until the action on the target. AI can analyze large data sets and molecular patterns to discover new molecules and compounds that may be useful for the treatment of diseases. It can also make the process of synthesizing these compounds easier to design.
Better diagnostics and more personalized treatments
AI can screen patient data and test results to identify accurate diagnoses. With AI, we can develop advanced diagnostic tools such as pattern identification in medical images and early disease detection. It is also being used for personalized therapies, with computer models predicting which treatments will be most effective in each particular case, tailoring them to the specific needs of each patient, reducing the negative impact or side effects of treatments and increasing their effectiveness.
Optimizing clinical trials
The biggest cause of delay in clinical trials comes from the patient recruitment process. The use of AI has made it possible to find suitable patients for clinical trials more quickly and efficiently, and also to ensure that they are the right candidates thanks to their correct segmentation based on eligibility, suitability, motivation and empowerment. This reduces the number of potentially unsuccessful trials, speeding up the research process and the time to market for new drugs.
Improving adherence and drug dosing.
This is achieved by predicting how new compounds will be absorbed by the body and how long they will remain in our body. This process is also improved thanks to the identification of drugs that can be used in different pathologies, which is called drug repositioning, or even the prediction, using Machine Learning (ML), of any biological property of a possible drug without the need to obtain it in the laboratory or conduct animal testing. Similarly, AI systems can monitor drug use and send reminders to patients to improve adherence to treatment and reduce dropout rates of prescribed medications.
The identification of drugs that can be used in different pathologies is a strategy that aims to discover new uses for drugs that have already been approved. Thanks to the reuse of these drugs, risks can be reduced and the development process can be speeded up. However, the combination of clinical trials can be costly and takes time to be considered effective. AI has the ability to generate a hypothesis faster and accelerate the clinical trial of a drug.
Creating cures for complex diseases and better treatments for rare diseases or pathologies known but without a cure.
Some examples of these diseases are amyotrophic lateral sclerosis (ALS), Alzheimer’s disease or Parkinson’s disease. Machine learning algorithms make it possible to integrate massive amounts of data from various sources, including clinical trials, patent records or other scientific data and publications, in order to repurpose existing drugs and apply them to address these lesser-known diseases.
Improving drug quality during manufacturing and compliance with standards
Thanks to the use of cameras and cognitive algorithms based on Deep Learning, pharmaceutical companies can analyze each product during the manufacturing process. We can detect and eliminate defects in real time ensuring compliance with quality standards and reducing CoQ or cost of quality.
Improved and proactive safety for workers
The use of AI with machine vision also allows us to detect any safety risks either to people or to the business. We can generate automatic alerts when a worker is not properly wearing his personal protective equipment (PPE), thus improving his safety and preventing health risks. We can detect and monitor access of people and vehicles to restricted areas or areas with risk of contamination. We can even use these systems to stop equipment and machines automatically to ensure safety.
Optimizing industrial operations and reducing waste
Another use case for combining AI with machine vision is to increase production visibility by detecting bottlenecks, delays or incidents that impact productivity and quality, estimate inventories, verify product packaging, and prevent or anticipate machine failures. The constant flow of this information significantly improves process visibility and the ability to make real-time decisions.
DISTRIBUTION AND COMMERCIALIZATION
Supply chain optimization
AI can help pharmaceutical companies optimize the supply chain, reduce costs and improve production efficiency. Through AI systems, it is possible to make an intelligent prediction of demand, optimize logistics and inventory, and even detect trends in new products, which makes it possible to cross-reference drug sales variables with user preferences. With the use of Big Data and AI, companies in the sector can analyze large amounts of information from different communication channels, enabling them to make better decisions, understand supply needs, anticipate market trends, and improve the accuracy of their current and future order predictions. A concrete example is pharmaceutical distributors who have to manage twice a day the orders requested by their network of pharmacies. With ML, they can predict the daily orders and prepare in advance the buckets to be shipped to the pharmacies, optimizing not only the delivery time but also indirectly their sales.
Detecting drug fraud
AI systems can analyze drug purchasing and supply patterns to detect fraud and abuse in the pharmaceutical market.
Managing communication with the support of virtual assistants and through new channels
Pharmaceutical distributors want to continuously improve their communication with their pharmacy network and are evaluating the suitability of conversational algorithms to respond to different call patterns handled by their call centers. Thanks to the new conversational virtual assistants, the user experience can be as close and familiar as in a conversation with a human, and the average call time can be reduced to 1 minute. Many of the requests can be redirected to the chatbot, such as queries about warehouse stock, order generation, delivery note printing or consumables requests. Another even more innovative option is to integrate these virtual assistants to new communication channels such as social networks, and in particular WhatsApp Business.
The future of AI
Predicting the future of a rapidly advancing industry is not easy, but what is certain is that AI will be an essential ally in driving new research, being key in tasks like the interaction of molecules, help in the creation of new drugs, support for clinical diagnosis or the optimization and personalization of therapies for patients. In the not too distant future, humans will not be used in pharmaceutical tests and AI will make it possible to test in just a few seconds the effect of tens of thousands of drugs that have been applied to physiological imitations of human bodies.
While AI has great potential to help redefine the pharmaceutical sector, its adoption is not straightforward as companies operating in this field are often unfamiliar with this technology. Added to this is the lack of updating and adaptation of technological infrastructures, which is a priority in the implementation and development of this type of solution. The success of AI adoption depends on the ability of pharmaceutical companies to build a strong organization capable of facing this digital transformation by investing in their technical infrastructure and data analysis capabilities.
knowmad mood and Artificial Intelligence
At knowmad mood we have more than 29 years of experience in the field of Information Technology. We have helped many clients in their digital transformation and specifically in the Health Sciences sector.
Our case studies
In one of the most important pharmaceutical distributors in the country, as part of its advanced analytics projects, we have optimized one of its most critical business processes, the forecasting of orders in advance, using ML. Initially we have performed the statistical analysis of data from 21 pharmacies over a period of 5 years to study the feasibility of predictability, and once demonstrated, we have performed a proof of concept to finally develop and implement the final solution.
The National Dosimetry Center (CND) has also relied on us for one of its most strategic projects. It has to respond to the need and obligation according to legal regulations to perform inter-territorial monitoring and establish a registry of the doses of ionizing radiation received by patients. In addition, it has to establish the reference levels in the country in a dynamic and updated way. For this purpose, we are assisting you in the creation of a secure information platform, which complies with GDPR and provides practitioners and scientists at international level with accurate information. We are designing the solution architecture, developing the various modules for dose recording, implementing the dose indicator research platform with its access management, extracting, transforming, loading and anonymizing data, and performing the generation, training and evaluation of various models with ML.
Another interesting case study of the use of AI is the one we are developing with the platform of companies and start-ups of one of the autonomous communities. Thanks to European funds dedicated to launching innovative projects and made available to this cluster, we have conducted a proof of concept with the aim of demonstrating the feasibility of a European project oriented to analyze the supply chain in the production of vaccines and to detect quickly and smoothly possible shortages in health resources. We have designed the cloud architecture, performed data ingestion, processing and visualization, and applied ML algorithm for prediction.