How Pharma AI Benefits the Time to Market for New Drugs

Artificial Intelligence is a rising star – meteoric, actually – in most industries, including pharma. But, do you really know how pharma AI can affect your business?

Pharma artificial intelligence can help you drive revenue growth and operational efficiency by powering in-depth data mining and analytics, patient engagement, compliance monitoring, and making efforts. It can impact your R&D time and costs, too!

A look at how AI transforms pharma company operations will help you make an informed decision if you haven’t adopted the technology yet. Perhaps, answers to the following questions will help see why AI can be a worthy investment:

• What pharma processes or elements can AI impact?
• What’s the role of machine learning in healthcare outcome monitoring?
• How does AI impact pharma speed to market?
• What’s the effect of AI in pharma sales productivity and revenue growth?

Let’s find answers by examining the most critical ways that pharma AI can affect the time to market for new drugs…

Let’s start with the good news:

1. Integrated Information Systems and Automated Big Data Mining Speeds Up Drug R&D

One of the most significant benefits of artificial intelligence in the pharma industry is rapid drug research and development.

It involves the use of machine learning (ML) and deep learning (DL) algorithms, which “train” integrated computer systems to automatically extract and analyze massive chunks of raw genotypic and phenotypic data relevant to drug R&D.

The system streamlines and fast tracks the collection of big data from the Internet of Things (IoT) devices (such as wearable health monitoring gadgets), medical research journals, patient management software, and other public and private databases.

AI systems that gather R&D data automatically and relatively fast helps to shorten the medicine discovery time, which is longer with a human-centric effort. Pharma companies can increase their speed to market and gain a competitive edge by delivering new drugs to hospitals and patients comparatively fast, which results in turnover growth.

2. Accurate Predictive Pharma Analytics Help Boost Clinical Trials Success Rates

AI-driven pharma R&D delivers predictive analytics, enabling researchers to pinpoint with a high degree of accuracy the right compounds for manipulating diseases. It provides in-depth insights and data necessary to validate and test drug concepts as well as optimize treatment delivery methods.

It can cost up to $2B to research, iterate, test, perfect, and validate a drug concept. However, the cost can come down with the accurate prediction of drug candidates, which increases the probability of clinical trial success.

AI-powered analytics helps to eliminate trial and error in the drug discovery process, and this can reduce overall R&D costs as it allows pharma researchers to present more viable solutions for approval by the FDA most of the time.

3. Tracking Medication Adherence

Low medication compliance1 rates among patients participating in a clinical trial can slow the drug R&D process. Likewise, the inability to monitor the extent to which each candidate is adhering to a prescribed drug can undermine the testing and validation phase.

Traditional health IT systems and human-centric approaches (such as requiring the patient to memorize their dosage) have not solved the non-adherence problem.

AI can help track pharma compliance rates in several ways. For example, indigestible IoT sensors transmit adherence information to a centralized database, enabling pharma researchers to monitor and analyze drug usage against treatment results and side effects.

The technology can track critical biometrics in the patient, including blood pressure and glucose levels. It can pinpoint anomalous outcomes based on the intelligence gathered via machine learning.

Another approach is facial recognition software. It involves a patient recording themselves taking a drug, after which the AI algorithm analyzes the video to confirm that the right candidate took the pill.

4. Improvement of Treatment Outcomes by Virtual Collaboration and Coaching

The success of any drug in the market depends on its ability to solve the intended healthcare problem. However, an otherwise appropriate medical solution may fail if the patient is not using it correctly out of ignorance, or certain lifestyle habits are hindering outcomes.

AI changes that.

A “smart” healthcare system can process natural language, and that makes it possible to “advise” patients in real-time in a language they understand, without necessarily involving a real, human doctor.

The system draws from vast amounts of healthcare data and medical records to provide highly personalized, evidence-based answers to questions that patients ask.

For example, robot-like assistants can help reinforce behavioral changes necessary for drug compliance and successful treatment. Advanced systems may also pose questions to patients, for example, to help understand why they’re skipping doses.

AI-powered IoT technology can then transmit the data to a pharma content management system for real-time monitoring. Improved treatment outcomes mean more business for pharma companies.

5. Streamlining Pharma Sales Process

AI-powered pharma sales software can impact reps productivity and turnover in a big way. For instance, the tech can help study industry trends and customer preferences, such as the treatment options a particular practice or doctor prefers the most.

A sales team may use that intelligence in pre-calling planning to gather relevant promotional and informational material. Effective pre-meeting preparation increases the chances of converting leads to sales. It can help boost pharma revenue.

The advantages of pharma AI nutshell…

Pharma AI speeds up the drug R&D process, enabling companies to differentiate their products by delivering healthcare solutions to hospitals and patients faster than otherwise possible. It reduces R&D costs while boosting clinical trial success rates, which translates to increases profit margins for pharma businesses.

Integrating IoT and AI into patient management solutions helps to improve medication adherence and treatment outcomes, resulting in in-market success. The tech can also boost pharma sales productivity and revenue.

Explore More Relevant Articles on P360

• 3 Things Pharma Companies Should Know About Territory Alignment
• 5 Use Cases for Commercial Pharma AI Beyond Drug Discovery
• 6 Important Pharma Data Collection Sources To Keep An Eye On
• How to Streamline Your Clinical Trial Management with Cloud Technology
• Exploring Data Fabric and Data Mesh in Business Operations

Now, for the bad news. There do happen to be a couple of slight detractors to using AI in the pharma industry:

1. Patients May Not Always Cooperate

For pharma companies to monitor drug use and effects using AI-powered technology, participating patients must be willing to interact with the system consistently.

For example, a candidate may decline to ingest a biosensor or film themselves taking a pill. Without collecting vast amounts of consistent patient data, ML algorithms cannot “learn” and extract the intelligence necessary to help pharma researchers or providers to draw accurate conclusions on treatment outcomes or drug efficacy.

2. Incompatible Legacy Health IT Infrastructure

The majority of current health IT systems do not naturally lend themselves to AI and big data mining applications.

With most of the healthcare data available in disparate, structured and unstructured sources, extracting in-depth insights for tasks like remote treatment outcome monitoring and patient engagement is usually difficult if not impossible.

As such, pharma companies have to develop (or acquire) interoperable health IT infrastructure before they can leverage AI to tap into data from diverse sources, and to extract predictive analytics/business intelligence, which enables them to make informed, real-time choices.

The fact of the matter is that presently, most healthcare systems are not AI-ready.

Not all patients or healthcare facilities may adopt the interoperable AI infrastructure needed to supply pharma companies with large volumes of data for in-depth ML analytics.

Leverage AI to Boost Your Overall Pharma Business Efficiency (and Profits)

Artificial intelligence in the pharmaceutical industry outperforms human-centric patient or customer data collection and analysis techniques. It’s a game-changing and product-differentiating piece of technology.

By incorporating AI into your pharma management system, you can increase business revenue and profit through enhanced speed to market, effective patient monitoring and compliance, and improved sales productivity. Contact us below and let’s chat!

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Reference

1: https://www.cbinsights.com/research/clinical-trials-ai-tech-disruption/