1.Manufacturing process improvement
In development and production, AI provides numerous opportunities to improve
processes. AI can perform quality control, shorten design time, reduce materials
waste, improve production reuse, perform predictive maintenance, and more.
AI can be used in many ways to make production more efficient with faster output
and less waste. For example, a process that typically relies on human intervention
to input or manage process data can be done using CNC (computer numerical
control). The AI machine learning algorithms not only ensure tasks are performed
very precisely, but also analyze the process to find areas where it can be
streamlined. This results in less material waste, faster production, and more
consistently meeting the product’s Critical Quality Attributes (CQAs).
2.Drug discovery and design
From designing new molecules to identifying novel biological targets, AI is playing a
role in drug target identification and validation; target-based, phenotypic, as well as
multi-target drug discoveries; drug repurposing; and biomarker identification. The
key benefit for pharma companies is the potential for AI, especially when
implemented during drug trials, to reduce the time it takes a drug to get approval
and reach the market. This can result it great cost savings, which could mean lower
cost drugs for patients, as well as more treatment choices.
For example, pharma researchers can identify and validate novel cancer drug
targets using data such as longitudinal electronic medical records (EMR records),
next generation sequencing, and other ‘omic data are used to create representative
models of individual patients.
Read more: How to optimize cell culture media to speed biopharma development
3.Processing biomedical and clinical data
Perhaps the most developed use of AI so far is in algorithms designed to read,
group and interpret large volumes of textual data. This can be a big time-saver for
researchers in the life sciences industry, providing a more efficient way to examine
the enormous amounts of data from the growing volume of research publications
in order to validate or discard hypotheses.
Furthermore, many clinical studies still rely on paper diaries in which patients log
when they took a drug, what other medications they took, and any adverse
reactions they had. Everything from handwritten notes and test results to
environmental factors and imaging scans can be collected and interpreted by AI.
The benefits of using AI in this way include faster research and cross-referencing of
data, as well as combining and extracting data into usable formats for analysis.
A Cognizant study showed that around 80% of clinical trials fail to meet enrollment
timelines, and one-third of all Phase III clinical study terminations are due to enrollment
difficulties.
4.Rare diseases and personalized medicine
Combing information from body scans, patient biology and analytics, AI is being
used in various ways to detect diseases such as cancer, and even predict health
issues people might face based on their genetics. One example is the IBM Watson
for Oncology, which uses each patient’s medical information and history to
recommend a personalized treatment plan.
AI is also being used to develop personalized drug treatments based on an
individual’s test results, reactions to past drugs and historical patient data for drug
reactions.
5.Identifying clinical trial candidates
Besides helping to make sense of clinical trial data, another use of artificial
intelligence in the pharmaceutical industry is finding patients to participate in the
trials. Using advanced predictive analytics, AI can analyze genetic information to
identify the appropriate patient population for a trial, and determine the optimal
sample size. Some AI technology can read free-form text that patients enter into
clinical trial applications, as well as unstructured data such as doctor’s notes and
intake documents.
6.Predicting treatment results
Among the more time- and cost-saving applications of artificial intelligence, is the
ability to match drug interventions with individual patients, reducing work that
previously involved trial and error. Machine learning models are capable of
predicting a patient’s response to possible drug treatments by inferring potential
relationships among factors that might be affecting the results, such as the body’s
ability to absorb the compounds, the distribution of those compounds around the
body, and a person’s metabolism.
7.Predictive biomarkers
Development of biomarkers is an important task not only in the context of medical
diagnostics, but also for the process of drug discovery and development. For
example, predictive biomarkers are used to identify potential responders to a
molecular targeted therapy before the drug is tested in humans. In this process, AI
uses biomarker models that are “trained” using large datasets.
8.Drug repurposing
For budget-pressed pharma companies, repurposing drugs promises to be one of
the most immediate areas that AI-based technologies can deliver great
value. Repurposing previously known drugs or late-stage drug candidates towards
new therapeutic areas is a desired strategy for many biopharmaceutical companies
as it presents less risk of unexpected toxicity or side effects in human trials, and,
likely, less R&D spend.
9.Drug adherence and dosage
Ensuring compliance to a drug study protocol by voluntary participants in clinical
studies is a huge problem for pharma companies. If patients in a drug study don’t
follow the trial rules, they must either be removed from the study or risk corrupting
the drug study results. One of the important factors of a successful drug trial is
ensuring that participants take the required dosage of the studied drug at the
prescribed times. That’s why having a way to ensure drug adherence is so
important. Both through remote monitoring and algorithms for evaluating test
results, AI can sort the good apples from the bad.
- R&D
Pharma companies around the world are leveraging advanced ML algorithms and AI-
powered tools to streamline the drug discovery process. These intelligent tools are
designed to identify intricate patterns in large datasets, and hence, they can be used to
solve challenges associated with complicated biological networks.
This capability is excellent for studying the patterns of various diseases and recognizing
which drug compositions would be best suited for treating specific traits of a particular
disease. Pharma companies can accordingly invest in the R&D of such drugs that have
the highest chances of successfully treating a disease or medical condition.
- Drug Development
AI holds the potential to improve the R&D process. From designing and identifying new
molecules to target-based drug validation and discoveries, AI can do it all.
According to an MIT study, only 13.8% of drugs are successful in passing clinical trials.
To top that, a pharma company has to pay anywhere between US$ 161 million to US$ 2
billion for a drug to get through the complete process of clinical trial and get FDA
approval. These are the two main reasons why pharma companies are increasingly
adopting AI to improve the success rates of new drugs, create more affordable drugs ad
therapies, and, most importantly, reduce operational costs. - Diagnosis
Doctors can use advanced Machine Learning systems to collect, process, and analyze vast
volumes of patients’ healthcare data. Healthcare providers around the world are using
ML technology to store sensitive patient data securely in the cloud or a centralized
storage system. This is known as electronic medical records (EMRs).
Doctors can refer to these records as and when they need to understand the impact of a
specific genetic trait on a patient’s health or how a particular drug can treat a health
condition. ML systems can use the data stored in EMRs to make real-time predictions for
diagnosis purposes and suggest proper treatment to patients.
Since ML technologies possess the ability to process and analyze massive amounts of
data quickly, they can help quicken the diagnosis process, thereby helping save millions
of lives.
Recently, in mid-April, the marketing of a medical device named GI Genius that is based
on Machine Learning and uses an AI algorithm was authorized by FDA. It is now being
utilized by clinicians to detect signs of colon cancer. With the help of this device, you can
easily detect portions of the colon, with potential lesions, during the time of
colonoscopy.
- Disease Prevention
Pharma companies can use AI to develop cures for both known diseases like Alzheimer’s
and Parkinson’s and rare diseases. Generally, pharmaceutical companies do not spend
their time and resources on finding treatments for rare diseases since the ROI is very
low compared to the time and cost it takes to develop drugs for treating rare diseases.
According to Global Genes, nearly 95% of rare diseases don’t have FDA approved
treatments or cures. However, thanks to AI and ML’s innovative abilities, the scenario is
rapidly changing for the better. - Epidemic prediction
AI and ML are already used by many pharma companies and healthcare providers to
monitor and forecast epidemic outbreaks across the globe. These technologies feed on
the data gathered from disparate sources in the Web, study the connection of various
geological, environmental, and biological factors on the health of the population of
different geographical locations, and try to connect the dots between these factors and
previous epidemic outbreaks. Such AI/ML models become especially useful for
underdeveloped economies that lack the medical infrastructure and financial framework
to deal with an epidemic outbreak.
A good example of this AI application is the ML-based Malaria Outbreak Prediction
Model that functions as a warning tool predicting any possible malaria outbreak and aid
healthcare providers in taking the best course of action to combat it.
- Remote Monitoring
Remote monitoring is a breakthrough in the pharma and healthcare sectors. Many
pharma companies have already developed wearables powered by AI algorithms that
can remotely monitor patients suffering from life-threatening diseases.
For instance, Tencent Holdings has collaborated with Medopad to develop an AI
technology that can remotely monitor patients with Parkinson’s disease and reducing
the time taken to perform a motor function assessment from 30 minutes to three
minutes. By integrating this AI technology with smartphone apps, it is possible to
monitor the opening and closing motions of the hands of a patient from a remote
location.
On detecting hand movement, the smartphone camera will capture it to determine the
severity of the symptoms (Parkinson’s). The frequency and amplitude of the movement
will determine the severity score of the patient’s condition, thereby allowing doctors to
change the drugs as well as the drug doses remotely.
In case the conditions become worse demanding a treatment upgrade, the AI will send
an alert to the doctor and arrange a checkup. Remote setups like these help eliminate the
need to travel back and forth to the doctor’s clinic, saving patients the hassle of traveling
and waiting.
- Manufacturing
Pharma companies can implement AI in the manufacturing process for higher
productivity, improved efficiency, and faster production of life-saving drugs. AI can be
used to manage and improve all aspects of the manufacturing process, including:
- Quality control
- Predictive maintenance
- Waste reduction
- Design optimization
- Process automation
AI can replace the time-consuming conventional manufacturing techniques, thereby
helping pharma companies to launch drugs in the market much faster and at cheaper
rates as well. Apart from increasing their ROI substantially by limiting the human
intervention in the manufacturing process, AI would also eliminate any scope for human
error.
- Marketing
Given the fact that the pharmaceutical industry is a sales-driven sector, AI can be a
handy tool in pharma marketing. With AI, pharma companies can explore and develop
unique marketing strategies that promise high revenues and brand awareness.
AI can help to map the customer journey, thereby allowing companies to see which
marketing technique led visitors to their site (lead conversion) and ultimately pushed
the converted visitors to purchase from them. In this way, pharma companies can focus
more on those marketing strategies that lead to most conversions and increase
revenues.
AI tools can analyze past marketing campaigns and compare the results to identify which
campaigns remained the most profitable. This allows companies to design the present
marketing campaigns accordingly, while also reducing time and saving money.
Furthermore, AI systems can even accurately predict the success or failure rate of
marketing campaigns.
Although AI is rapidly finding applications in the pharma industry, the process of
transformation is not without challenges. Usually, the current IT infrastructure of most
pharma companies is based on legacy systems that aren’t optimized for AI.
Moreover, the integration and adoption of AI demand industry expertise and skills,
something that is still not readily available. However, the process of AI adoption in the
pharma sector can be made easy by taking these steps:
- Partnering and collaborating with academic institutions that specialize in AI R&D to
guide pharma companies with AI adoption. - Collaborate with companies that specialize in AI-driven medicine discovery to reap
the benefits of expert assistance, advanced tools, and industry experience. - Train R&D and manufacturing teams to use and implement AI tools and techniques
in the proper way for optimal productivity.
18.Drug Adherence And Dosage
The use of artificial intelligence in pharmaceutical industry is growing at an
unprecedented pace. AI in pharma is now being used to identify the right amounts of
drug intake to ensure the safety of drug consumers. It not only helps to monitor the
patients during clinical trials but also suggests the right amount of dosage at regular
intervals.
Artificial intelligence in pharmaceuticals has led to faster automation in processes and is
one of the key factors behind the increasing need for accuracy in this industry. The
opportunities available for AI in pharma are unmeasurable and ensure both efficiency
and compliance. Furthermore, AI in pharmaceuticals has also unlocked several
potential AI jobs for people that come with lucrative salaries and benefits.
Future Of Artificial Intelligence In Pharmacy
Researchers indicate that by the year 2025, almost 50% of global healthcare facilities
will be implementing this technology in their business operations. Such is the growth
of artificial intelligence in pharmacy. Drug developmental companies are expected to
invest more in this technology for finding innovative solutions to chronic and oncology
diseases. Some of the major chronic diseases that are expected to be tackled by Artificial
Intelligence include diabetes, cancer, and chronic kidney diseases.
AI is also expected to improve the current candidate selection processes for clinical trials
through faster assessments and identifications of the best patients for a given trial.
Experts can harness the power of AI to provide more valuable information from the data
provided for their patients. This includes MRI images, and mammograms as well. While
all of these will surely revolutionize the whole industry of healthcare, there is an
additional benefit of lots of AI jobs that will be available in the future, for people who
specialize in this field. With the increasing adaptability of this technology, accessibility to
the same will no longer be an issue, and it will slowly and steadily become a part of the
natural process within pharmaceuticals and manufacturing.
