BlogsChallenges of AI/ML Adoption in the Pharma Industry

January 27, 2022by Shashiraja
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The pharmaceutical industry depends majorly on ground-breaking and innovative technologies for delivering reliable and safe drugs in the market. In addition, during the recent pandemic, there has been a demand for getting drugs and vaccines out in the market faster.

Artificial intelligence and machine learning (AI/ML) play an essential role in the pharmaceutical world for varied applications like disease identification, diagnosis, personalized treatment, drug discovery and manufacturing, predictive forecasting, drug safety, efficacy, etc. AI/ML is gaining importance in the pharma industry due to its advanced capabilities, improved computing powers, and increased data availability. This can help in streamlining processes and driving decisions swiftly. 

Role of AI/ML in the Pharma Industry

The importance of AI/ML has increased over the last several years. The pharma and the biotech industries use this AI/ML technology for their efficient working, automating processes and boost faster decision-making capabilities. The role of AI/ML in the different sections of the pharma industry includes

Manufacturing: AI/ML is beneficial in drug manufacturing and the production stage as it helps in improving the processes. It enables shortening of the designing time, reducing material waste, improving production reuse, etc. All these processes lead to increased production with lesser waste and faster output.

Retail and Distribution: AI/ML allows optimization of the inventory levels at all points based on the demand patterns. This helps the pharma industries understand the consumers’ demands and their location to provide faster deliveries. Using AI/ML improves the quality, safety, access, transparency across the different levels of supply chain management for the drug.

Consumers: Patients react to the various drugs differently. Sensory AI helps predict, understand, and optimize consumer bespoke preferences. It aids in building a relationship between the consumers and the desired product experience.

Industry 4.0 in Pharma Industry

The pharma industry incorporates the features of industry 4.0 to enable the systems that include little or no involvement of humans. It allows integrating and connecting the internal and external information. External information consists of the patient experience, supplier inventories, market demand, etc. The internal information includes energy and resource management, laboratory data, modelling and simulation outcomes, etc. Integrating this information leads to unprecedented responsiveness, control, monitoring, and prediction.

Industry 4.0 allows the implementation of digitalization and digital maturity. It comprises the interconnection of computing devices, instruments, sensors, equipment that are integrated online using a cohesive network for cyber security. 

AI/ML enables the use of computer-based intelligence like reasoning, learning, problem-solving, and decision making. It aids in inspection procedures, quality control testing, analytical testing, process quality assurance, data integration, etc. AI is also applied in the pharma industry for disease identification, diagnosis, digital therapeutics, forecasting, drug manufacturing, clinical trials, etc.

AI/ML Adoption Can Bring Massive Value

There are different areas in the pharma industry where AI/ML will be useful, majorly in drug development and manufacturing. The most promising results for AI are achieved in the areas of drug development include:

  • Data-driven target discovery
  • Pre-clinical and early-stage drug discovery
  • Next-generation sequencing
  • Late-stage drug candidates
  • Small molecule therapeutics
  • Novel drug design
  • Novel biological targets

AI/ML allows in streamlining the process of manufacturing and production in the different pharma companies in the areas like

  • Consistent quality control, meeting critical quality attributes (CQAs)
  • Improved waste management
  • Shortened design phase
  • Supply chain management
  • Improved production reuse
  • Inventory management
  • Predictive management

AI/ML is also helpful in processing clinical and biomedical data, personalized medicines and rare diseases, etc.

AI/ML Adoption Challenges in the Pharma Industry

Pharma companies face various challenges for the wide-scale adoption of AI and machine learning. These challenges are related to logistical, technical, regulatory, and other aspects.

Regulatory challenges: Every company needs to follow specific regulatory frameworks to face regulatory barriers. Filling the regulatory applications across the different global jurisdictions has different regulatory expectations.

Technical challenges: Different technical challenges include the inflexibility of process parameters, offline testing applications, human involvement in manufacturing operations, etc. These challenges are dependent on the framework implemented and the lack of available technologies.

Logistical challenges: The number of skills required may be beyond the traditional chemistry, biology, and process engineering aspects. It requires extensive communication and cooperation between the regulators and manufacturers for proper utilization.

Unfamiliar technology: For most pharma companies, the use of AI/ML is very new and appears as a black box that they need time and effort to understand this new, unfamiliar technology.

Lack of IT infrastructure: Using AI/ML requires updated IT applications. The current pharma industry does not have the IT developed for handling the AI/ML facility. These companies need to make investments for upgrading their IT system for incorporating AI/ML features.

Low accuracy of training data: The algorithms used in AI/ML have a higher threshold for minimizing the errors, but some definite errors may still arise from the training sets.

Overfitting and underfitting: Using algorithm prediction can have issues like overfitting or underfitting. In the case of overfitting, the model has lower quality information but generates high-quality performance. In the case of underfitting, the model fails in recognizing the trend, thus providing inaccurate results.

Benefits of AI/ML in the Pharma Industry

Using the AI/ML techniques in the pharma industries has various added benefits like:

  • AI/ML can be used for balancing drug prices by understanding the market trend and the required compliances.
  • Controlled substances are required to follow regulations. AI/ML allows identifying the high risk of diversion for these controlled substances.
  • AI/ML helps the pharma companies analyze the inventory levels and predict drug overages and shortages. Based on this, the company can plan their manufacturing and stocking of the drugs.
  • With AI/ML, the pharma companies can optimize their drug distribution to the detailers for an adequate supply of medicines.
  • AI/ML enables pharma companies to identify novel targets for faster drug discovery and target identification.
  • AI/ML provides insight into the drug dosing in clinical trials and its correlation with the reactions.
  • AI/ML allows identifying the possible pattern observed in case of side effects.In short, emerging technologies like AI and machine learning have a great potential for transforming the working of pharma companies. For example, it can improve pharmaceutical supply chain management, drug development, manufacturing, distribution, inventory, etc. It is the key to providing customized services based on conditions and requirements.Currently, these technologies are new, and the industry is facing some adoption challenges. But with professional support from solution providers like DforD, one can overcome several such challenges and also modernize business by leveraging DforD’s AI based data exchange platform to interconnect and exchange data in real time.
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