The pharmaceutical industry is facing a range of challenges that necessitate a significant shift in how it operates. These challenges include increasing costs, aging populations, and a growing demand for personalized medicine. In response to these challenges, many pharma companies are turning to digital technologies to help them overcome obstacles and stay competitive.
Digital transformation in the pharma industry involves the use of technology to streamline operations, enhance patient engagement, and improve the efficiency of clinical trials. It also involves the adoption of different methods like data analytics and integration, artificial intelligence, machine learning, cloud computing, and services, etc. to drive innovation and improve patient outcomes.
The pharmaceutical industry has been a pillar of healthcare for decades, but with the advancement of digital technologies, it is undergoing a major transformation. Digital technologies have the potential to disrupt every aspect of the pharma industry, from research and development to marketing and sales.
Pharmaceutical companies are faced with the challenge of effectively managing and analyzing large volumes of data, which come in various formats, in order to extract meaningful and actionable insights. This is crucial for efficient drug development that balances cost and effectiveness. The use of cutting-edge technologies can help uncover the mechanisms of diseases, optimize clinical trials, and improve production efficiency and accuracy. By utilizing data analytics and automation, pharmaceutical companies can optimize their manufacturing processes and ensure timely and reliable delivery of drugs to patients. In addition, by leveraging data analytics and machine learning, pharma companies can identify inefficiencies in their manufacturing processes and take data-driven actions to improve efficiency and minimize waste. This can result in substantial cost savings and enhanced product quality. By utilizing real-time monitoring and data analytics, pharma companies can also monitor the drug's movement from production facilities to patients, reducing the risk of counterfeit drugs and improving the supply chain's overall efficiency.
As the number of players in the research and development process grows, having a centralized database that can access various sources and databases is increasingly important. Therefore, being able to manage and integrate data generated at every stage of the process, from design to end-user, is a top priority for any forward-thinking pharmaceutical company.
Artificial intelligence (AI) is a type of technology that employs complex algorithms and software to imitate human intelligence and process large amounts of complex health and medical data. AI is being used in various fields of medicine, especially in the areas of diagnosis and treatment protocols. The use of automated algorithms in pharmacy is redefining the traditional roles that used to rely on human intelligence. In recent years, AI has revolutionized the pharmaceutical industry by allowing scientists to discover new drugs, develop treatments, and find innovative solutions to some of the biggest challenges in healthcare.
Artificial intelligence has the potential to optimize production and prevent supply chain disruptions, as well as perform quality control and predictive analytics, reducing waste and correcting breakdowns in the production line. In addition to these benefits, AI can also help personalize treatment through mobile apps that track health metrics and allow for remote monitoring, improving research and development and treatment efficacy.
Pharmaceutical companies are increasingly turning to automated data collection and analysis to tackle complex challenges in drug development. By utilizing AI algorithms, scientists can map the complex roles that hundreds of genes play in individual diseases, and monitor the effects of drug treatments on human cells starting from the preclinical phase. This early understanding of a drug's effectiveness can help improve the drug development process and potentially lead to faster approval and better patient outcomes.
Additionally, facial and image recognition algorithms can be used to monitor therapy adherence and optimize treatment outcomes. AI can also detect potential side effects much earlier.
Recruiting suitable patients for clinical trials is often challenging for large pharmaceutical companies. However, AI and machine learning can be employed to extract useful data from patient records, simplifying the process. The monitoring of participants could also be improved. Wearable devices and remote monitoring technologies enable researchers to gather real-time data on patient outcomes, leading to more accurate and timely analysis of trial results. This translates into more effective treatments and better patient outcomes.
The implementation of AI in the pharmaceutical industry is rapidly increasing, with a USM Systems study indicating that around 50% of healthcare companies worldwide intend to adopt AI strategies and deploy the technology widely by 2025.
Drug development is a time-consuming process that involves analyzing and investigating compounds for their biological and chemical properties. However, machine learning has emerged as a valuable tool for accelerating this process. With access to large databases, machine learning algorithms can extract chemical and biological information to identify compounds that are worth exploring further. This technology is being increasingly used by research teams to predict the potential of untested compounds. Through modeling QSAR (quantitative structure-property relationships) and developing AI programs, modern machine learning techniques can accurately predict how chemical modifications will affect biological behavior.
Machine learning is also used in research and development to improve pharmacokinetic and pharmacodynamic information, such as absorption, distribution, metabolism, excretion, mechanisms of action, route of administration, side effects and toxicity, demographic variations, and interactions with other drugs.
By leveraging the speed and accuracy of computers, new drugs can be developed more quickly and cost-effectively compared to traditional manual methods for example by using machine learning to speed up drug development by predicting the efficacy of untested compounds through image analysis. These advancements in technology are paving the way for more efficient drug development and providing hope for the treatment of previously untreatable diseases.
According to a 2013 report by McKinsey, machine learning has the potential to save the pharma and medicine industry approximately $100 billion annually in the US alone through higher efficiency in clinical trials, better decision-making, and innovative tools that can help consumers, doctors, regulators, and insurers make informed decisions.
Computers are playing an increasingly significant role in pharmaceutical development. They are becoming a more convenient tool for analyzing biological interrelationships and therapeutic potential and providing a quick and thorough screening of large libraries of compounds to identify the desired structure. Over time, specific computational techniques such as virtual screening, de novo design, and fragment-based drug development have been introduced. These techniques are being employed to optimize the drug development process. Additionally, computing is being used to manage and analyze data in preclinical studies, resulting in increased productivity and shorter development time.
Computing has also become an essential tool for faster and more accurate communication within clinical trials. Due to the enormous amount of information generated within a trial, pharmaceutical companies are transitioning from traditional paper-based methods to electronic systems. This shift is facilitated by various software applications designed to manage clinical trials, including clinical trial management systems (CTMS), clinical data management systems (CDMS), pharmacovigilance systems for ensuring drug safety, and electronic data collection tools (EDCT). With the help of these software tools, clinical trial teams can efficiently manage and analyze data, leading to increased productivity and faster decision-making.
By implementing various data sharing and exchange systems, communication between different databases and teams is made possible, enabling the analysis and export of data in different formats for the purpose of preparing reports or future planning. With the integration of smart devices and data sharing systems, clinical trial outcomes can be improved while increasing efficiency. There are different methods of integrating data, including software, tools, and services. Cloud computing services have become increasingly popular among pharmaceutical companies as they offer high computing power and allow partners to easily access, share, and manage data. This technology provides an attractive solution for pharmaceutical companies seeking to streamline their clinical trial processes and improve outcomes.
Cloud services are particularly useful in telemedicine, mobile health applications, and remote monitoring tools. Despite what many people think, this is not just a storage solution, but a network that enables other technologies such as AI, smart embedded devices, and databases to connect in real-time, with purposes like data integration, telemedicine, and robotic surgery.
Cloud computing technology offers benefits like decreased reliance on internal infrastructures and improved streamlining of processes on a global scale. Its applications are widespread, spanning the entire product life cycle. During the preclinical stage, data from multiple sources can be easily and safely collected and stored in the cloud. In clinical trials, it enables greater transparency and real-time visibility of operations and data.
The digital transformation in the pharma industry is a result of the collaborative efforts of various players, including startups, tech companies, established pharma companies, and healthcare providers. Each of these players has a unique perspective and skill set that is critical to achieving digital transformation in the industry.
Startups and tech companies are known for their innovative digital platforms and technologies that can improve patient outcomes and streamline operations. These companies often partner with pharma companies to develop and implement digital solutions that can transform the industry.
Established pharma companies are also key players in driving digital transformation. Many of them invest in digital capabilities either through internal development or partnerships with startups and tech companies. They also incorporate digital technologies into their existing operations, from drug development to supply chain management.
Healthcare providers play a critical role in digital transformation in the pharma industry. They increasingly utilize digital technologies to enhance patient outcomes and increase efficiency, ranging from telemedicine to digital health platforms. Additionally, they can provide valuable insights into patient needs and preferences, making them important partners for pharma companies.
While digital technologies have the potential to revolutionize the pharmaceutical industry, the transformation process is challenging. The challenges faced by pharmaceutical companies are numerous and complex. They are often related to the unknown nature of the technologies, insufficient experience and knowledge, and the need for substantial investments in upgrading the entire IT infrastructure.
Furthermore, to fully utilize the potential of these technologies, a lean organization within eHealth, including electronic records, is necessary. There must be a large set of accumulated data, greater transparency between individual healthcare processes, and solutions to issues related to cybersecurity and personal data access and distribution.
The digital transformation of pharma raises also some ethical concerns that must be addressed to ensure patient privacy and autonomy. It is crucial for companies to collect and use patient data in a responsible manner, while also making digital solutions accessible to patients regardless of their socioeconomic status or location.
Real-time work and increased communication and collaboration between various stakeholders in separate processes also complicate the integration of data, while security risks increase. Despite these challenges, pharmaceutical companies are increasingly partnering with large technology firms with cloud and artificial intelligence solutions. As a result, more pharmaceutical giants are investing in technology companies.
As the pharmaceutical industry grows larger, it requires increasingly complex and advanced technological infrastructure to operate. The applications of digital technologies in the lifecycle of medicinal products have the potential to transform the control and management systems of pharmaceutical companies. Artificial intelligence, machine learning, cloud computing, and cloud services have all become essential components of modern pharmaceutical companies.
The main goal of this transformation is to reduce development costs, shorten cycle times, and improve the quality of medicinal products through improved data integration and real-time status updates. By connecting all stages of the information delivery cycle, pharmaceutical companies can increase their efficiency and productivity.
The benefits of digital transformation in pharma are clear: it can speed up and improve the accuracy of drug development, increase patient engagement and adherence, and enable the delivery of more personalized healthcare.
However, there are significant challenges that must be addressed. Pharmaceutical manufacturing processes must adhere to strict safety and reliability requirements, and technological solutions require corresponding infrastructural and cognitive requirements for successful integration. By overcoming these challenges, pharma companies can benefit from improved patient outcomes, reduced costs, and increased innovation, thus contributing to the betterment of the industry and the overall healthcare ecosystem.
Nevertheless, these challenges are unlikely to halt the trend towards increasing implementation of digital technologies in all industries.
Reference Literature:
1. Reinhardt IC, Oliveira JC, Ring DT (2020) Current perspectives on the development of industry 4.0 in the pharmaceutical sector. J Ind Inf Integr 18:100131
2. AI in the Pharma Industry: Current Uses, Best Cases, Digital Future, 2021; Available at: https://pharmanewsintel.com/news/ai-in-the-pharma-industry-current-uses-best-cases-digital-future
3. Top AI Use Cases In Pharma and Biomedicine, 2020; Available at: https://usmsystems.com/ai-in-pharma-and-biomedicine/
4. Gartner Forecasts Worldwide Artificial Intelligence Software Market to Reach $62 Billion in 2022, 2021; Available at: https://www.gartner.com/en/newsroom/press-releases/2021-11-22-gartner-forecasts-worldwide-artificial-intelligence-software-market-to-reach-62-billion-in-2022#:~:text=Gartner%20Forecasts%20Worldwide%20Artificial%20Intelligence%20Software%20Market%20to%20Reach%20%2462%20Billion%20in%202022,-Market%20Growth%20Will
5.Laman, Fuad. (2021). Pharmaceuticals Data Analysis by Machine Learning. 10. ISSN: 2319-7463.
6. Jang, Ha Young & Song, Jihyeon & Kim, Jae & Lee, Howard & Kim, In-Wha & Moon, Bongki & Oh, Jung. (2022). Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information. npj Digital Medicine. 5. 10.1038/s41746-022-00639-0.
7. Lo YC, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23(8):1538–1546
8. How big data can revolutionize pharmaceutical R&D, 2013; Available at: https://www.mckinsey.com/industries/life-sciences/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d
9. Ebrahimi Hariry, Reza & Barenji, Reza & Paradkar, Anant. (2021). From Industry 4.0 to Pharma 4.0. 10.1007/978-3-030-58675-1_4-1.
10. Computers in Preclinical development, 2018; Available at: https://www.clinskill.com/computers-in-preclinical-development/
11. Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Chem 12(1):1–23
12. The Increasing Use Of AI In The Pharmaceutical Industry, 2020; Available at: https://www.forbes.com/sites/cognitiveworld/2020/12/26/the-increasing-use-of-ai-in-the-pharmaceutical-industry/?sh=72b132fd4c01
13. Big pharma is using AI and machine learning in drug discovery and development to save lives, 2022; Available at: https://www.insiderintelligence.com/insights/ai-machine-learning-in-drug-discovery-development/
Svetoslav is a Medical doctor, master in public health, and business expert with over 17 years of experience in the pharmaceutical industry. Held various positions at the level of vice president, executive director, and director in multinational companies at global and regional levels (USA, Europe, Asia, Australia) in the field of management, sales department, corporate strategies, market access, relations with institutions, marketing, R&D . In his work, he places a strong focus on leadership, market development, customer and patient orientation, innovative solutions and strategic thinking. Lecturer in international and national forums on various topics, such as gene and cell therapies, innovative models of new molecule development, healthcare economics, and trends in clinical trial development. Former member of the EFPIA Working Group for Central and Eastern Europe, Chairman of the ARPharM Management Board, and Co-Chair of the AmCham Health Commission. He is currently a member of the management board of the Bulgarian Oncology Scientific Society, National Coordinator of the European Union initiative "Europe beats cancer" for Bulgaria, lecturer at the Medical Universities, managing director of Sunlight Health - a company with a focus on the healthcare sector.
