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The integration of AI into the pharmaceutical industry is a rapidly growing practice. From discovery and development to production and manufacturing, biopharmaceutical companies aim to create as many potential candidates for clinical development as possible—and as quickly as possible.

This is resulting in increased partnerships between widely recognized leaders in biopharmaceuticals and AI-based drug companies that can deliver new efficiencies to their processes using deep learning, natural language processing (NLPs), and machine learning (ML).

The application of AI in the pharmaceutical industry is central to the field’s expected growth. Valued at $905.91 million in 2021, the implementation of AI in pharmaceuticals is expected to increase to $9.241 billion at a compound annual growth rate (CAGR) of 29.4% from 2022 to 2030. We’ll discuss eight of the top applications of AI in pharmaceutical manufacturing and how they are changing healthcare for the better.

8 Significant Implementations of AI and ML in the Pharma Industry

1. Drug Discovery

AI can assist researchers in analyzing extensive datasets to identify molecular properties that may have the desired therapeutic value with great precision and speed.

2. Drug Design

AI is used to develop new drug designs when creating novel chemistry, identifying potential interactions and outcomes, and modeling the testing of drugs in human and animal models before running final trials.

3. Manufacturing Processes

Agile manufacturing relies upon multiple separate processes operating in tandem at maximum capacity. If operations begin to malfunction or slow, it can hinder other parts of production, impacting the overall efficiency of an entire facility.

AI is used in pharmaceutical manufacturing in numerous ways, including:

Process Automation

AI is used to streamline production throughout all phases of manufacturing. Where repetition and maintaining a continuous pace can be overly taxing on human operators, AI is used to ensure consistency in redundant processes.

Waste Reduction

AI assists in closely analyzing batches of drugs throughout production stages to determine where improvements can be made, either in equipment functioning or in processes.

Predictive Maintenance

By continuously monitoring all stages of manufacturing operations, AI can identify when/where certain changes in output, timing, or other equipment behavior are signs that parts need to be replaced or repaired. Preventative maintenance is more effective than scheduled maintenance alone as it can address problems before they halt operations, and a sign of wear or required service updates could also signal a cyber-attack directed at machinery.

4. Clinical Trials and Data Processing

AI improves multiple facets of pharmaceutical clinical trial processes. These systems are able to substantially reduce the duration of clinical trial cycles using predictive models and analytics to discern the most relevant and valuable information within real-world data (RWD). AI helps drug developers better understand diseases and patient groups, as well as accelerating coding, analysis, management, and storage of clinical data.

Electronic data capture (EDC) reduces human error in collecting and integrating data into expansive databases.

AI is used in clinical trials to model data variables and run simulations that identify potential pitfalls in a given assessment. Furthermore, implementing AI enables pharmaceutical manufacturers to analyze massive quantities of clinical trial data, comparing independent data from trials of multiple drugs or therapies to aid in designing for ideal future outcomes.

5. Rare Diseases

AI plays a significant role in the early diagnosis of rare diseases. The more data researchers gather about the characteristics of disease, the greater their potential to develop pharmaceutical remedies that are more effective at earlier stages of an illness’ progression.

For example, self-supervised image search for histology (SISH) has proved highly successful in identifying early onset symptoms of disease. This deep learning algorithm is trained on a data set of existing imaging, enabling it to conduct a precise search for similar results in immense pathology image repositories.

This data can be combined with similar research in discovering molecular traits that are referenced when developing new drug formulas that may cure diseases or treat their symptoms.

6. Personalized Medicine

Predictive analytics are used in personalized medicine, also referred to as precision medicine, to include each individual’s particular genetic traits in a bespoke disease prevention regimen. AI helps uncover valuable data influenced by lifestyle factors and family history so that physicians may design effective drug regimens to protect and prolong individual health.

7. Identifying Clinical Trial Candidates

AI plays a significant role in identifying optimal candidates for clinical trials. It is used to sift through large volumes of medical health records and generate the most promising prospects based on an assortment of desired attributes.

An example is topological data analysis (TDA), often implemented to identify subgroups of patient conditions inside a larger category. This functionality can be used to search for specific health conditions amongst a set of people sharing a common disease, or to isolate side effects and other conditions for individuals within a mutual demographic.

8. Predicting Treatment Results

AI has multiple uses beyond collecting and infering relevant information from large datasets; it is increasingly designed to offer deep insights into genomic data, biomarkers, medical images, and medical records, assiting with prevention procedures and treatments.

Benefits of AI in the Pharmaceutical Industry

AI, deep learning, NLP’s, and ML are helping to augment the accuracy and efficiency of processes, research, and even diagnoses. AI has a positive impact across all sectors of healthcare. Specifically within the pharmaceutical field, AI provides the following benefits:

1. Accelerated discovery of new drugs

2. Personalized treatments for each individual based on genetics and other factors

3. Increased efficiency in drug research and during clinical trials

4. Automated manufacturing processes to reduce production costs

5. Reduced production errors and costly equipment repairs

6. Improved quality control

7. Ensured drug compliance through automated reporting and auditing

8. Lowered risk of human error by automating redundant processes

The Impact of Nanotronics’ Process Control Innovations

Nanotronics’ automated optical inspection systems and process control tools streamline processes in pharmaceutical manufacturing to improve yield in high-demand, high-volume production.

Our AI-powered nControl™ process control system empowers you to inspect, correct, and protect processes better than fixed-formula models, adapting to changes in environmental conditions across your facility.

nControl™ software pairs with nSpec® hardware to revolutionize manufacturing through Nanotronics’ full-stack factory control solution, AIPC™. By integrating this advanced system into your facility, you are able to track and predict KPI’s such as yield, waste, energy use, and uptime.

Our AI platform identifies gaps in processes, addresses contamination risks, and helps identify potential malicious threats, while our proprietary ML technology employs supervised learning, data augmentation, and pattern recognition to maximize your data integrity and security.

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