Shots:

• Driven by the influx of life-changing innovations and technological advances, the pharmaceutical industry is forging ahead at a momentous pace. With the impelling Pharma 4.0, automation has now been embedded in various aspects of current business practices 

• Delving deeply into the importance of automation, primarily focusing on AI & ML, PharmaShots unveils its eight-step efficacious roadmap, designed especially for pharmaceutical companies 

• PharmaShots along with its parent company Octavus Consulting work extensively on several uptake models for the life science, biotech, pharmaceutical, MedTech, and animal health industry  

In the era of endless innovations and inducive cutting-edge technological transformations, industries are fostering a holistic approach to integrate automation into their current practices. The road to implementation can be challenging for both big as well as small companies. Where factors like short-term funding from investors and protocol variability restrict small companies from infusing automation into their companies, conglomerates on the other hand have aced up their sleeves by partnering with the automation industry. Intelligent automation comprises Artificial Intelligence (AI), Business Process Management (BPM), and Robotic Process Automation (RPA). The article focuses on the integration of automation with Artificial Intelligence (AI) and Machine Learning (ML). This article provides an informative account of intelligent automation, the key differences between AI & ML, their importance, and a potent eight-step roadmap to implementation.   

Key Differences between AI and ML 

AI and ML are often used together though each term signifies a different aspect of technology. Before we proceed with our article let us first understand a few key differences.

How Important are AI & ML for Pharmaceutical Industry? 

Automation offers a substantial advantage to the pharmaceutical sector over the traditional operational model. Automation helps companies save considerable money on data refinement, drug discovery, patient selection, and more. With this integration, companies can recalibrate their strategies and focus more on potential drugs with higher success ratios. Let us discuss the importance of automation in brief. 

• Diagnosis: A major spike is witnessed in partnership initiatives among tech companies and the pharmaceutical industry to provide early diagnosis of diseases. Such joint ventures have been highly successful in terms of business and scalability. Mentioned below are a few notable partnerships in the healthcare industry 

• Northwestern Medicine and Google Health: Google Health works in collaboration with Chicago-based Northwestern Medicine to develop an AI algorithm for the diagnosis of lung cancer as well as breast cancer 

• Mayo Clinic and IBM Watson Health: The partnership aims to develop an AI algorithm to diagnose patients with cancer by using the patient population of the Mayo Clinic 

• NVIDIA and Massachusetts General Hospital: This partnership aims to diagnose conditions like cancer and pneumonia by developing algorithms for radiology 

• GE Healthcare and UC San Francisco: University of California, San Francisco collaborated with GE Healthcare in developing AI algorithms for medical imaging 

• Drug Discovery: Automation helps screen potential compounds to expedite the drug discovery processes. Major companies use this approach to find the right compound and check its effects on patients based on biological factors. Automation has deeply influenced the way we earlier saw multifactorial disorders. With the advent of precision medicines to treat gene-based conditions, automation has profoundly helped patients with such disorders 

• Clinical Trials: Machine learning has a significant advantage in clinical trial research. It uses advanced predictive analytics to identify candidates with its wide range of data and leveraging genetic information, resulting in quicker and cost-effective clinical trials. Moreover, machine learning remotely monitors and opens the door to real-time data for increased safety  

• Radiology and Radiotherapy: Automation aims to transform the science of radiology and radiotherapy. Google’s DeepMind Health in collaboration with University College London Hospital (UCLH) is developing algorithms to detect differences in healthy and cancerous tissues 

• Epidemic Outbreak Forecast: With a coalesce of real-time social media updates, historical information, and data derived from satellites, AI and ML’s fusion can help predict diseases and measure the severity of the epidemic 

• Manufacturing: When it comes to manufacturing, Pharma companies can benefit significantly by infusing AI and ML into several aspects. For instance, AI and ML can help in process automation, quality check, predictive maintenance, and waste reduction 

• Marketing: By leveraging automation for the marketing of pharmaceutical products, companies have benefitted significantly. From the assessment of past marketing campaigns to designing an effective and novel campaign, intelligent automation has truly revolutionized the ways of marketing 

Several pharmaceutical companies have benefitted colossally by opening the door to automation. Covering all the AI-based pharmaceutical companies is beyond the scope of this article. The image below represents the leading AI-based pharmaceutical companies 

A Holistic Approach to Automation 

Implementing AI & ML, particularly in the pharmaceutical industry, can be challenging. Companies need to adopt a holistic approach to exploit the best results. PharmaShots brings an elaborate eight-step plan specially designed for pharmaceutical companies. Let us closely examine each crucial step to get a clear understanding of the plan.  

• Define Objectives: Outline a comprehensive report on the objectives you seek through the implementation of AI and ML. Moreover, consider specific areas and aspects by highlighting challenges, be it drug discovery, procurement & supply chain management, or clinical trials 

• Data Preprocessing: Preprocess and consolidate data collected from clinical trials, genomics, and public data repositories by refining and normalizing. Feature engineering can be used to enhance the predictive capabilities of the model  

• Selection, Development & Evaluation of Model: Uptake models should be selected based on the company’s needs and should be developed under the supervision of experts and must be evaluated from time to time to check the progress in every area 

• Integration & Deployment: While integrating the trained and evaluated model into existing infrastructure and workflow, companies must ensure the deployment process is as per the set regulatory requirement 

• Monitoring & Maintenance: By constantly monitoring the deployed model and frequently updating the new data, companies can evaluate the proper functioning of the model as per the set criterions 

• Partnerships & Collaborations: Often, pharmaceutical companies partner with AI companies to effectively implement the technological aspect into workflows. Such collaborations are important indeed to keep up in the era of cutting-edge technology. 

• Regulatory Compliance: Companies must emphasize the guidelines set by the regulatory body, be it FDA or EMA, to ensure good manufacturing practices. Companies are compliant to follow the standards set by the regulatory authorities to ensure drug safety and clinical decision-making 

• Ethical Compliance: Post implementation, pharmaceutical companies must comply with certain ethical aspects such as privacy, transparency, and biased mitigation.  

Conclusions & Perspectives 

Automation, especially in the pharmaceutical industry, requires immense expertise and a dedicated team to handle different areas. Moreover, the companies that opted for automation have benefitted significantly from it. Companies on shoestrings may find it rather difficult to collaborate with AI-based companies. In such cases, companies may initially begin by infusing small aspects of Robotic Process Automation (RPA) into essential areas like R&D, clinical trials, and patient data entry. PharmaShots along with its parent company Octavus Consulting help life science companies with a comprehensive roadmap to automation with efficacious uptake models. For more information email us at connect@pharmashots.com  

References: 

Javatpoint 

EMERJ 

P360