Artificial Intelligence or AI in the pharma industry presents various opportunities to substantially improve the pace of drug discovery and distribution process. The current protocol followed needs to be upgraded in order to meet the rising demand for medicine and that too without compromising its quality. Advanced AI solutions will help pharma companies to process structured and unstructured data in order to derive useful and actionable insights.
The application of machine learning and AI to drug discovery will not only accelerate the process but also help companies to spawn a higher return on investment. It will make it easier for scientists to find potential targets and for the manufacturers to ensure its timely delivery. McKinsey estimates that machine learning and big data can help to generate a profit of around $ 100 billion for the pharma industry. The insights produced with the help of analytics would help the pharma companies to make better decisions, improve the efficiency of clinical trials, advance the shipping process and ultimately achieve greater commercial success.
AI in the pharma industry is the use of algorithms, computer vision technologies, and automation to speed up tasks that were traditionally performed by humans. The pharma and biotech industry saw huge investments in artificial intelligence in recent times. From market research to drug development and cost management, AI is playing a vital role in modernizing the pharma industry and bringing new drugs faster into the market.
Big data and AI-based advanced analytics have brought a radical change in the pharma sector. Faster innovation, increase in productivity, and building comprehensive supply chain systems are possible with artificial intelligence.
According to a study conducted by the Massachusetts Institute of Technology (MIT), less than 14% of the new drugs pass clinical trials. Moreover, the pharma company has to pay billions to get the drug approved by the government authorities. By using artificial intelligence in pharmaceutical research and development, pharma companies can increase their success rate. The data from clinical trials are collected and processed using AI and ML systems to derive insights about the drug and its reactions to the test subjects.
The positives and side effects are carefully observed and analyzed to make the necessary changes to the drug’s composition. This will result in drugs with a better curing capacity and fewer side effects.
The pharma industry requires billions to keep up the R&D. The company spends huge amounts of money at every stage to ensure that the drug is made using quality materials and in hygienic and sterile conditions. The warehouse for storing inventory should have a temperature control facility to maintain the necessary conditions for the drugs to retain their original composition.
By adopting artificial intelligence software apps and integrating them with systems in the pharma company, the management can streamline the process from start to finish. This will reduce operational costs and minimize the risk of damaging the drugs.
Let’s take Novartis as an example. The pharma company is investing in AI and ML to find ways to speed up the treatment processes and help patients become healthier. The company is working on classifying digital images of cells based on how they are responding to treatment (compounds).
The ML algorithms collect the research data and group cells with similar responses to the compounds used for the treatment. This information is then shared with the research team to help them use the insights and their experience in understanding the results. Novartis uses the images developed by machine learning algorithms to run predictive analytics and identify cells that may not respond to the treatment.
The ML algorithms make it easier to study large amounts of data and identify the patterns of different diseases, their impact on the cells and organs, the symptoms, and the possible treatment methods/ drugs that can cure the diseases. A pharma company that invests in adopting artificial intelligence at each level (R&D, production, supply chain, etc.) will have an edge over competitors and can provide expensive drugs for cost-effective prices to make treatment affordable for more patients.
Look how ML and AI models are transforming the pharma industry and making it even better than before.
Optimization of the supply chain across pharmaceutical industries has always been a challenge for the owners. However, with the advent of AI and ML, the process is becoming smoother. The big data generated helps companies to reach out to their prospective clients and understand their needs, which in turn ensures the number of drugs to be produced by the companies.
Also, predictive analytics insights generated with the help of big data allow the companies to foresee the demand pattern and hence manufacture only the required quantity of medicines. The drugs today are being increasingly customized for even small populations with particular genetic profiles.
Finding out a way to deliver a medicine that is relevant only to a small bunch of 1000 people is more difficult than delivering medicines across the world. This venture requires proper utilization of resources so that there is no delay in delivery and loss to the company. An expert at “LogiPharmaUS Conference” in 2017 said that “Instead of executing one supply chain a thousand times, we should get ready to execute a thousand supply chains, one at a time.”
This act will not only ensure timely drug delivery but also safeguard the hassle of re-execution every time. Machine learning and AI algorithms can help to automate this process and make it more robust.
Now, when we talk only about shipping drugs, there are many medicines that are expensive and require very specific conditions to be transported. Billions and trillions of money are spent by the pharma companies to deal with the transportation process. With the application of ML and AI pharma, companies will be able to forecast demand and distribute products efficiently. Also, many key decisions will become automated allowing the companies to cut down their labor costs and make more profit.
Unlike other products, the manufacturing of drugs requires more attention. The temperature, pressure, fermentation time, etc. all have to be kept in check so that the vaccines/drugs that are being produced meet the market standards and offer the desired results. If even one condition is disrupted, the entire batch has to be discarded which accounts for a huge loss in money as well as labor for the companies.
With the help of machine learning and artificial intelligence solutions, pharma companies are able to identify the characteristics that may stand responsible for drug degradation. For instance, Merck has placed a data-collection technology to “identify the root cause of batch non-confirmation issues” which has compared the batches over 5.5 million times. By doing this the professionals have been able to identify the early fermentation phase of vaccine production that serves as a strong predictor of the quality of the vaccine. Thus these advanced analytics algorithms allow the pharma industry to meet strict quality standards without making any compromises. Also, the data from the previous batches helps to understand the key factors that may have been responsible for the failure of drug formation in previous cases. This insight helps the company owners to ensure that no such errors/mistakes are made in the future that may hamper the drug’s quality.
A drug takes around 12 years to reach the market, during this time it undergoes various processes: target identification, drug formation, and clinical trials. Out of these identifying the target is what consumes the maximum time the scientists. The disease-causing organisms keep on evolving rapidly and hence their genetic makeup changes as well. This makes it challenging for scientists to find targets against which inhibitors have to be designed.
Earlier scientists had to go through a lot of literary studies to identify the patterns and then create a hypothesis on which the research could be based. With the advent of machine learning and artificial intelligence, there are ways in which scientists can retrieve the sequenced genes and run them against each other to predict conserved domains (potential targets). These predicted targets can then be used to create drugs against them.
All the process that use to take years of hard work is now just a few clicks away. This not only speeds up the process of drug creation but offers other insights related to the disease as well. Talking the other way around, AI and ML may also assist companies to foretell the chemical compound that may be used against the predicted targets.
After around 100 years of advancements in medical science, life expectancy and quality have surely been improved. But somehow now, the pharma industry is witnessing stagnant growth, probably because a larger number of drugs need to be manufactured and reached out to patients all over the world.
At AI consulting firms such as DataToBiz, AI professionals focus on helping the pharma industry to improve its logistics and manufacturing components. They can help industries to keep a track of their shipment so that medicines reach the public on time.
The experts may also assist the pharma industry by utilizing all their previous data and predicting the demand patterns of a certain medicine prior to its production. All these methods will not only help the pharma industries to create quality products but also assist them in efficiently distributing them all over the globe.
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
