How Artificial Intelligence (AI) and Machine Learning (ML) Concepts are Transforming Generic Pharmaceuticals
Dr. Sam Raney, Associate Director for Science, Office of Research and Standard Office of Generic Drugs recently presented on Generic Drug Science & Research Priorities for Fiscal Year (FY) 2023. During his presentation, he listed “Expand the Use of Artificial Intelligence (AI) and Machine Learning (ML) Tools” as one of the priorities for FY 2023.
In Dr. Raney’s presentation, he offered 3 specific examples of how AI can be used by the generic drug players:
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To improve the use of real-world evidence for post-market surveillance of generic drug substitution and to evaluate the impact of generic drugs on public health
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By integrating AI/ML tools with information and data available to the FDA and identifying strategies to optimize the reliability of outcomes produced by these tools
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To explore the capability of AI/ML tools for a prospective applicant to be able to efficiently assess the completeness of its ANDA prior to submission, and to enhance the efficiency, consistency, and quality of regulatory assessments once ANDAs are submitted.
Patrizia Cavazzoni, director of the Center for Drug Evaluation and Research (CDER) stated that, “artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work.”
- Repurposing Existing Drugs/Drug Discovery: AI algorithms can analyze large volumes of biomedical data, including scientific literature, clinical trial results, and genomic information, to identify potential drug targets and predict the efficacy and safety of drug candidates. This can significantly accelerate the drug discovery process and help identify potential alternatives to existing drugs.
Drug discovery process is non-linear in nature and starts with drug target identification, compound screening, preclinical/non-clinical, clinical research to product launch and pharmacovigilance. Innovations such as AI/ML can help analyze and synthesize large amounts of data from manuscripts, research articles, publications, and other available data sources to facilitate drug development programs.
AI/ML can help predict the chemical properties and biological activity of screened drug candidates and predict the efficacy and affinity for a target. Similarly, AI/ML can help identify new use of an existing drug, i.e. repurpose existing generic drug molecules in case of 505(b)(2) application.
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Formulation Development: AI can aid in the formulation development process by analyzing data on the physicochemical properties of drug molecules and excipients. It can optimize drug formulations to improve drug delivery, stability, and bioavailability, leading to more effective and efficient drug products.
Here are some key aspects where AI/ML can be applied in the formulation development process:
Predictive Modeling: AI/ML algorithms can analyze vast amounts of data on the physicochemical properties of drug molecules and excipients to develop predictive models. These models can estimate important formulation parameters such as solubility, stability, bioavailability, and drug release profiles. By leveraging these models, formulation scientists can make informed decisions during the development process, saving time and resources.
Formulation Optimization: AI/ML can assist in optimizing drug formulations by considering various factors, including drug properties, excipient selection, and process parameters. These algorithms can explore the vast formulation design space, identify optimal combinations, and generate formulations with desired characteristics. This can help streamline the formulation development process and improve the quality of drug products.
Quality by Design (QbD): QbD is an approach that focuses on understanding and controlling formulation variables to ensure product quality. AI/ML techniques can help in QbD implementation by analyzing experimental data, identifying critical formulation attributes, and establishing correlations between formulation variables and product performance. This knowledge can guide the formulation development process and facilitate robust formulation design.
Stability Prediction: AI/ML can aid in predicting the stability of drug formulations under various storage conditions. By analyzing historical stability data and incorporating other relevant parameters, such as temperature, humidity, and packaging, these algorithms can predict the degradation pathways and estimate the shelf life of a formulation. This information can guide formulation, development and improve product quality and stability.
Data Integration and Analysis: AI/ML can integrate and analyze diverse data sources, including scientific literature, patent databases, and formulation databases. By extracting valuable insights and patterns from these data, formulation scientists can gain a better understanding of formulation strategies, identify formulation challenges, and leverage existing knowledge for new
formulation development.
- Process Optimization, Manufacturing, Quality Control and Supply Chain Optimization: AI can optimize manufacturing processes in pharmaceutical production by analyzing real-time data from sensors and production lines. It can identify patterns, detect anomalies, and suggest adjustments to improve efficiency, reduce waste, and ensure consistent product quality.
The use of AI/ML to support pharmaceutical manufacturing can be deployed together with other advanced manufacturing technologies such as process analytical technology (PAT).
Here are some of the ways that AI/ML is being used in pharmaceutical manufacturing:
Quality Control: AI/ML can be used to automate quality control checks, identify potential defects, and ensure that products meet regulatory standards. For example, AI can be used to analyze images of drug products to identify defects that would be difficult for humans to see. AI-powered image recognition algorithms can be employed to automate quality control inspections in pharmaceutical manufacturing.
Process Optimization: AI/ML can be used to optimize manufacturing processes, identify areas for improvement, and reduce waste. For example, AI can be used to analyze data from manufacturing equipment to identify patterns that can be used to improve the efficiency of the process.
Recommendation Systems: AI/ML can be used to develop recommendation systems that can help manufacturers select the right materials, equipment, and suppliers. For example, AI can be used to analyze data from previous manufacturing runs to recommend the best materials for a particular product.
Predictive Maintenance: AI/ML can be used to predict when equipment is likely to fail, so that preventive maintenance can be performed. This can help to avoid costly downtime and ensure that products are produced on time.
Supply Chain Management: AI/ML can be used to optimize supply chain management, ensure that products are available when needed, and reduce costs. For example, AI can be used to analyze data from sales and inventory levels to predict future demand. AI algorithms can optimize supply chain management by analyzing demand patterns, inventory levels, and distribution networks. This can help ensure timely availability of drugs, reduce costs, and minimize supply chain disruptions.
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Pharmacovigilance: AI can analyze vast amounts of real-world data, such as electronic health records, adverse event reports, and social media posts, to identify potential safety issues associated with generic drugs. This can help pharmaceutical companies and regulatory authorities monitor drug safety more effectively and take appropriate actions if necessary.
By applying AI/ML to the vast amounts of data generated during the pharmacovigilance process, scientists can gain new insights into the relationships between drug exposure and adverse events. This information can then be used to identify and assess new safety signals more quickly and efficiently, and to improve the risk-benefit assessment of drugs.
Here are some of the ways that AI/ML is being used in pharmacovigilance:
Data Processing and Analysis: AI can be used to process and analyze large datasets of individual case safety report (ICSR), which can help to identify new safety signals more quickly and efficiently.
Signal Detection: AI/ML models can be used to identify new safety signals from large datasets of adverse event reports. This can be a challenging task, as adverse event reports are often incomplete and unreliable. However, AI/ML models can be trained to identify patterns in data that would be difficult for humans to detect.
Risk Assessment: AI/ML models can be used to assess the risk of adverse events associated with a particular drug. This can be done by taking into account factors such as the drug’s mechanism of action, the dose, the patient population, and the adverse event data.
Predictive Modeling: AI/ML models can be used to predict the risk of adverse events in individual patients. This can be done by taking into account factors such as the patient’s medical history, the patient’s genetic profile, and the patient’s drug exposure.
Drug Safety Surveillance: AI/ML models can be used to monitor the safety of drugs in real time. This can be done by tracking adverse event reports, social media, and other sources of information.
The use of AI/ML in pharmacovigilance is still in its early stages, but it has the potential to revolutionize the field. By automating many of the tasks involved in pharmacovigilance, AI/ML can help scientists to identify and assess new safety signals more quickly and efficiently. This could lead to a more timely and effective identification of drug safety risks, which could improve the safety of drugs for patients.
Despite these challenges, the potential benefits of AI/ML in pharmacovigilance are significant. As the field continues to develop, we can expect to see AI/ML become an increasingly important tool in the
identification and assessment of drug safety risks.
- Regulatory Compliance: AI can assist in regulatory compliance by analyzing regulatory guidelines and ensuring that pharmaceutical products meet the necessary requirements. It can aid in the preparation of regulatory submissions, assess compliance risks, and facilitate adherence to regulatory standards.
Regulatory Intelligence: AI algorithms can analyze and extract
relevant information from regulatory guidelines, public databases, and FDA communications to provide regulatory intelligence. This can help pharmaceutical companies stay up-to-date with the latest regulatory requirements, including specific guidelines and formats for eCTD submissions.
Data Extraction and Standardization: AI/ML can automate the extraction and standardization of data from various sources for inclusion in the eCTD. This can involve extracting information from documents, labeling data fields, and ensuring consistency and accuracy in the submission. By automating these tasks, AI/ML can save time and reduce the potential for errors in the eCTD filing.
Document Classification and Organization: AI/ML techniques can be employed to classify and organize documents within an eCTD submission. This can involve automatically identifying and categorizing different document types (e.g., clinical studies, safety reports, quality documentation) and arranging them according to the appropriate eCTD structure. AI/ML can help streamline the organization process and ensure compliance with the FDA’s structure and requirements.
Quality Control and Validation: AI/ML algorithms can assist in
quality control and validation of the eCTD submission. They can
analyze the submission for completeness, consistency, and adherence to regulatory requirements. By automating these checks, AI/ML can help identify potential errors or omissions in the eCTD before submission, minimizing the risk of regulatory issues and delays.
Submission Tracking and Management: AI/ML can support the tracking and management of eCTD submissions by providing real-time monitoring and alerts. It can track the progress of the submission, provide notifications for key milestones or deadlines, and flag any potential issues or discrepancies. This can help ensure that theeCTD filing process is managed effectively and any deviations or delays are promptly addressed.
It’s important to note that while AI holds tremendous potential in the pharmaceutical industry, it is still an evolving field. Challenges such as data quality, regulatory considerations, and ethical concerns need to be addressed for the successful implementation of AI in this domain.
In May of 2023, the FDA published a discussion paper on Using Artificial Intelligence and Machine Learning in the Development of
Drug and Biological Products.
The FDA is currently accepting comments on the Federal Register portal to enhance mutual learning and to establish a dialogue with FDA stakeholders on this topic. You can put your comments here or review comments from various stakeholders.
In its discussion paper, the FDA highlighted potential concerns and risks associated with AI/ML. The FDA and other stakeholders are making significant efforts to develop general principles, standards, and practices for the use of AI/ML in drug development. The FDA is considering a risk-based approach to evaluate and manage the use of AI/ML to facilitate innovations while also bringing safe and effective new drugs to protect public health.
Here are some of the biggest challenges with using AI/ML in pharma:
Data Availability: The development of AI/ML models requires large datasets of pharmaceutical data. However, this data is often proprietary and difficult to obtain.
Model Validation: It is important to validate AI/ML models before they can be used in practice. This can be a challenge, as it requires the collection of additional data to test the model’s accuracy.
Interpretability: AI/ML models are often black boxes, which means that it is difficult to understand how they make their predictions. This can be a challenge for scientists who need to understand the reasons behind the model’s predictions in order to trust the results.
Bias: AI/ML models can be biased if they are trained on data that is not representative of the population. This can lead to inaccurate predictions and unfair decisions.
Regulation: The use of AI/ML in pharma is regulated by the FDA. This can make it difficult for companies to develop and deploy AI/ML models.
Despite these challenges, the potential benefits of AI/ML in pharma are significant. As the field continues to develop, we can expect to see AI/ML become an increasingly important tool in the development and manufacturing of safe and effective drugs.
Together, we can harness the power of AI/ML to revolutionize generic pharmaceuticals. Let’s embrace this transformative technology and shape the future of the generic pharmaceutical industry for the benefit of patients worldwide. Contact WiTii Consulting below to take a step into the future of generic pharmaceuticals.
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