NAVIGATING THE FUTURE OF MEDICINE: EXPLORING THE LATEST BREAKTHROUGHS IN THE PHARMACEUTICAL INDUSTRY FOR A BRIGHTER TOMORROW
HTML Full TextNAVIGATING THE FUTURE OF MEDICINE: EXPLORING THE LATEST BREAKTHROUGHS IN THE PHARMACEUTICAL INDUSTRY FOR A BRIGHTER TOMORROW
T. Bana, P. Kadu * and P. Pimple
Mrs. Saraswati Wani College of Pharmacy, Ganegaon, Ahmednagar, Maharashtra, India.
ABSTRACT: The pharmaceutical industry is undergoing a significant transformation due to technological advancements, scientific breakthroughs and evolving healthcare needs, paving the way for more effective treatments and improved patient outcomes. AI and ML are revolutionizing drug discovery and development by analysing vast datasets, predicting drug interactions and accelerating the identification of potential therapeutic compounds, reducing costs and improving clinical trial design and data analysis. Improvement of public health through revolutionizing the pharmaceutical industry is envisioned as multidirectional, encompassing utilization of AI and Data Analytics. The industry has strengthened in terms of drug discovery, personalized medication development and production enhancement through streamlined procedure adoption. Drug safety checks and data driven synthesis through streamlined processes offer substantial ground for future progressions in the industry. The present study explores various dynamics of the pharmaceutical industry from the perspective of developmental evolution both, on short term periods and over the period of decades. Incorporation of technology in aspects such as medication personalization, research and development as well as production capacity and precision has been noted. The article further proceeds to provide brief yet intimate exposures to the diversified details associated with industrial operations from corporate point of views as well. Cost breakdown of the combined industry has been observed and facilitation of executive decision-making through data-driven approaches is recognized. Distinct architectural frameworks such as SpuMoNI can be incorporated by industry players for intelligent data analytics, data quality and end-to-end verification.
Keywords: Technological advancements, AI, ML, Drug interactions, Clinical trial
INTRODUCTION: The pharmaceutical sector is going through a significant shift as a result of scientific discoveries, technological improvements and changing healthcare demands.
These developments are reshaping medical practice and indicating a brighter future with more potent therapies, individualized healthcare and better patient outcomes.
A paradigm change in healthcare is represented by precision medicine. It adopts medical interventions and treatments for specific individuals depending on their lifestyles and environmental circumstances rather than using a one-size-fits-all strategy. Drug research and discovery are being revolutionized by “artificial intelligence (AI) and machine learning (ML)”. With the help of this cutting-edge gene-editing technique, hereditary illnesses may one day be cured entirely.
Technology Lighting the Way to Healthier Futures: The topic “Technology Lighting the Way to Healthier Futures” appropriately captures how technological breakthroughs will shape how medicine and healthcare are delivered in the future. Technology lighting the path conveys a feeling of direction and guidance while highlighting how technology is shining the way to better healthcare. It suggests that technology is a shining example of advancement. A better future for people and society is the ultimate objective of the pharmaceutical business and medical technology 1. These subject stresses how technology has improved health outcomes. As the world’s population is expanding quickly, technology is advancing and managing energy resources efficiently is becoming ever more crucial 2. The modern world is driven by innovations, to stay relevant and continue producing high- quality medications that delight patients, pharmaceutical businesses must be prepared to adopt the most recent technology advancements 3. The chemical reactions and characteristics of potential new medication prospects are predicted by AI-driven algorithms 4, 5.
The timeline for drug discovery has been significantly shortened, costs have dropped, and profitability has grown all at the same time as illustrated in Fig. 1. Some digital medicines, such as integrating pharmacological treatments with non-drug treatments, are assisting patients in adopting a more comprehensive strategy to control their ailments 6, 7, 8. At present, the foundation for collaborative development with a wide variety of participants, including start-up enterprises, is the use of digital technology in drug discovery research 9, 10, 11. The COVID-19 vaccine developed by Modern Pharmaceuticals is a spectacularly effective example of using digital technology. This startup attracted attention for creating a cloud-based digital framework for rapid connectivity messenger RNA, or mRNA, drug discovery studies and for accelerating the procedure from pharmaceutical target selection to market distribution. This business, a pharmaceutical firm that was born in the digital age, actively uses digital technology to create vaccinations.
FIG. 1: COST BREAKDOWN OF THE COMBINED PHARMACEUTICAL INDUSTRY
Tracking the Path for Tomorrow's Health:
Pioneer Progress: This part perfectly encapsulates the exploration of the most recent pharmaceutical sector innovations for a more promising medical future. The path- tracking expression denotes a methodical and forward-looking strategy, putting special emphasis on the investigation and directionality of technological breakthroughs in the pharmaceutical sector. The future of healthcare and well-being is the primary focus, underlining the ultimate objectives of these discoveries to build a healthier and better future 12, 13. The pharmaceutical industry is a very data-intensive sector of the economy that often uses and produces a wide range of data Fig. 2. Every day and night, the amount of data has been growing tremendously. The sources of pharmaceutical data are also continually expanding. The pharmaceutical industries collect raw data from a variety of sources both inside and outside the company, including research, R&D, clinical trials, academic, medical device, and patient procedures 14, 15.
FIG. 2: METRICS ANALYSIS, TREND IDENTIFICATION AND DECISION MAKING THROUGH DIGITALIZATION
Due to the numerous complexities in processes, systems, functions, geographies, operations, etc., planning and executing big data analytics in the pharmaceutical business is difficult. Business executives must create a successful adoption and implementation plan for big data if they want to benefit from it in the pharmaceutical sector 16. Here elaborately discuss the applications of big data in the pharmaceutical industry for brighter improvement in future.
Applications of Big data in the pharmaceutical industry Fig. 3.
Increase Drug Development and Research: Big data analytics have previously been used by a portion of the pharmaceutical sector to improve a web-based search of massive databases of old, new, and outdated patents and pertinent research articles 17, 18.
Enhance Drug Clinical Studies: The Pharmaceutical Industry is frequently persuaded to shorten the duration of clinical studies. Clinical studies, however, are expensive and time-consuming. Large studies with the right patient mix are required.
Create Personalized and Targeted Medications: Big data may integrate genetic information, observed medical data gathered via an instrument that may be carried to monitor an individual's changes in appearance during therapy and digital health records of information 19.
Improve Risk and Safety Management: Data signals through online sources such as social media, may serve as preliminary indications regarding the safety of a recently introduced drug by the pharmaceutical business 20, 21.
Decrease Cost and Drug Utilization: The specifics of pharmaceutical analytics can aid pharmaceutical companies in making more informed decisions that will boost sales and cut costs.
FIG. 3: APPLICATIONS OF BIG DATA IN THE PHARMACEUTICAL INDUSTRY
Exploring Pharma’s Promising Frontiers:
Discovering Tomorrow's Health Vision: For many years, the pharmaceutical industry has been a major force behind the advancement of science and technology. The sector has throughout the years shown exceptional development and adaptability to its persistent dedication to improving healthcare and raising the quality of everyday life 22. The pharmaceutical industry is a shining example of human ingenuity and tenacity to ground-breaking discoveries in machine learning as well as artificial intelligence. The pharmaceutical industry remains at the forefront of this kind of advancement in the following significant ways. Among the latest breakthroughs in the industry, development and large-scale usage of mRNA vaccines are noteworthy 23. Technology has played a crucial role in aspects such as synthesis of RNA molecules that exhibit high purity 24, 25. Efficient use of LNPs (Lipid Nanoparticles) based mRNA delivery and analyses of genetic makeup of viruses for quick vaccination development.
The illustration below highlights efficient use of intelligent agents in pharmaceutical manufacturing lines. Implementation of SpuMoNI framework Fig. 4 has been noted for ensuring end-to-end verification, data quality and intelligent data analytics 26, 27. Data-driven synthesis and manufacturing is a promising aspect for widespread development of the international pharmaceuticals production industry.
FIG. 4: SPUMONI FRAMEWORK SPECIFICATIONS AND ELEMENTS
Big Data collection and analyses processes have been adopted across the industrial segment in discussion. The table provided below illustrates the overview of the sources used for data collection and pharmaceutical product development. Primary data is gathered through amalgamation of information collected by multiple-sensor equipped manufacturing processes along with results of laboratory experimentations 28. Table 1 summarizes the primary data sources. Process time series and intermediate product characteristics are conducted followed by quality analysis.
TABLE 1: DATA SOURCES AND PHARMACEUTICAL PRODUCT DEVELOPMENT
Incoming materials |
Excipients characteristic |
Active pharmaceutical ingredient characteristic |
Process |
Process time series |
Intermediate product characteristic |
Final product |
Laboratory analysis |
Product quality |
Personalized medicine market has inflated over the span of the past few years. The stated increase in personalized medicine has been noted with respect to the global markets and estimated in terms of billion U.S. Dollars. As of 2022, the global personalized medicine market has been evaluated to be worth an estimated amount of 2,770 billion USD Fig. 5 29. Exponential growth of the market size is indicative of active assistance gained by the industry through incorporation of modern advancements within the structural and functional frameworks.
FIG. 5: GLOBAL MARKET SIZE FOR PERSONALIZED MEDICINE 2015-2022 IN BILLION USD
The R&D landscape of the pharmaceutical industry has been reshaped and evolved through the past two decades. Biotechnology has rose to prominence since the early years of the past decade management and operations of biotech companies have been undergoing disruptive changes to facilitate R&D, investment opportunities, innovation pipeline and workforce motivation 30. The illustration below showcases the portfolio model that has emerged through the past decade highlighting over 5.5 billion USD capital raised in the past 6 years Fig. 6.
FIG. 6: BIOTECH PORTFOLIO MODEL
The overall advancements within pharmaceutical research, synthesis, production and distribution sectors have been summarized through the succeeding points Fig. 7.
Biotechnology Advances: The biotechnology field has advanced significantly impact in the pharmaceutical industry, including the creation of biological medicines like antibodies that are monoclonal and gene treatments 31. These procedures are among the most cutting-edge ones that are currently accessible.
Genomic Medicine: Determining the genetic causes of illnesses has become more possible as a result of the decoding of the genome of an individual. Targeted therapeutics with fewer adverse effects have been developed by pharmaceutical corporations using genetics as a tool 32.
Vaccines: The creation of vaccinations that have revolutionized public health was greatly aided by the pharmaceutical sector. Instances from recent times involve COVID-19 mRNA vaccines, which have shown the effectiveness of quick vaccine production.
Data Analytics and AI: AI and data analytics are being used more and more by pharmaceutical companies to find potential medication candidates, improve clinical trials, and tailor treatment methods 33, 34. Drug research and discovery are being accelerated by these tools.
Systems of Drug Delivery: Nanotechnology and implantable medical equipment are two examples of how improvements in drug delivery methods have increased medicine efficacy while reducing negative effects 35.
Clinical Trials: In-depth clinical studies are conducted by the pharmaceutical industry to evaluate the safety of new medicines 36. To improve the procedure, the industry also looking into cutting-edge trial designs for decentralized trials.
Digital Health: To improve patient care and different types of clinical research, the sector is embracing digital health technology including electronic health records and telemedicine.
Initiatives for Global Health: Pharmaceutical firms participate in global health projects, such as giving low-income nations access to necessary medications and working with other industries to fight illnesses like dengue and HIV/AIDS 37.
FIG. 7: THE PHARMACEUTICAL INDUSTRY CONTINUES TO BE AT THE FOREFRONT OF ADVANCEMENTS IN VARIOUS WAYS
CONCLUSION: The future of medicine gleams with remarkable promise. From the intricate depths of biotechnology to the personalized touch of precision medicine, a wave of innovation is transforming how we diagnose, treat, and manage illness. We stand on the precipice of a world where healthcare is more efficient, tailored to individual needs, and accessible to all. However, navigating this exciting terrain requires careful consideration. Ethical frameworks must guide our exploration, ensuring responsible development and equitable access. Governmental policies need to adapt to support this evolving landscape, while financial concerns demand solutions that ensure these advancements reach those who need them most. By addressing these challenges, we can usher in a future where the brilliance of medical progress illuminates the lives of all.
ACKNOWLEDGEMENT: Nil.
CONFLICT OF INTEREST: The authors declare no conflict of interest, financial or otherwise.
REFERENCES:
- Haverinen J, Keränen N, Falkenbach P, Maijala A, Kolehmainen T and Reponen J: Digi- HTA: Health technology assessment framework for digital healthcare services. Finnish Journal of eHealth and eWelfare 2019; 11(4): 326-341. http://dx.doi.org/10.23996/fjhw.82538
- Nižetić S, Djilali N, Papadopoulos A and Rodrigues JJ: Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management. Journal of Cleaner Production 2019; 231: 565-591. https://doi.org/10.1016/j.jclepro.2019.04.397
- Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB and Cohen AB: Digital health: a path to validation. NPJ Digital Medicine 2019; 2(1): 38. https://doi.org/10.1038/s41746-019-0111-3
- Yang X, Wang Y, Byrne R, Schneider G and Yang S: Concepts of artificial intelligence for computer-assisted drug discovery. Chemical Reviews 2019; 119(18): 10520-10594. https://doi.org/10.1021/acs.chemrev.8b00728
- Tripathi MK, Nath A, Singh TP, Ethayathulla AS and Kaur P: Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Molecular Diversity 2021; 25: 1439- 1460. https://doi.org/10.1007/s11030-021-10256-w
- Jagannath S, Mikhael J, Nadeem O and Raje N: Digital health for patients with multiple myeloma: an unmet need. JCO Clinical Cancer Informatics 2021; 5: 1096-1105. https://doi.org/10.1200/cci.20.00145
- Gomes M, Murray E and Raftery J: Economic evaluation of digital health interventions: methodological issues and recommendations for practice. Pharmacoeconomics 2022; 40(4): 367-378. https://doi.org/10.1007/s40273-022-01130-0
- Shi W, Cheng L and Li Y: Influence of “hospital-community-family” integrated management on blood pressure, quality of life, anxiety and depression in hypertensive patients. Computational and Mathematical Methods in Medicine 2022; Article ID 1962475. https://doi.org/10.1155/2022/1962475
- Calza F, Ferretti M, Panetti E and Parmentola A: Moving drug discoveries beyond the valley of death: the role of innovation ecosystems. European Journal of Innovation Management 2020; 24(4): 1184-1209. http://dx.doi.org/10.1108/EJIM-11-2019-0342
- Feola R, Cucino V and Parente R: How to develop collaboration in drug development process: the role of professional service firms. International Business Research 2021; 14(4): 114-125. https://doi.org/10.5539/ibr.v14n4p114
- Gillespie JJ, Privitera GJ and Gaspero J: Biopharmaceutical entrepreneurship, open innovation, and the knowledge economy. Journal of Innovation Management 2019; 7(2): 59- 77. https://doi.org/10.24840/2183-0606_007.002_0005
- Esposito C, Di Napoli I, Agueli B, Marino L, Procentese F and Arcidiacono C: Well- being and the COVID-19 pandemic. European Psychologist 2022; https://psycnet.apa.org/doi/10.1027/1016-9040/a000468
- Voukelatou V, Gabrielli L, Miliou I, Cresci S, Sharma R, Tesconi M and Pappalardo L: Measuring objective and subjective well-being: dimensions and data sources. International Journal of Data Science and Analytics 2021; 11: 279-309. https://doi.org/10.1007/s41060- 020-00224-2
- Yeung AWK, Atanasov AG, Sheridan H, Klager E, Eibensteiner F, Völkl-Kernsock S, Kletecka-Pulker M, Willschke H and Schaden E: Open innovation in medical and pharmaceutical research: a literature landscape analysis. Frontiers in Pharmacology 2021; 11: p.587526. https://doi.org/10.3389/fphar.2020.587526
- Facile R, Muhlbradt EE, Gong M, Li Q, Popat V, Pétavy F, Cornet R, Ruan Y, Koide D, Saito TI and Hume S: Use of Clinical Data Interchange Standards Consortium (CDISC) standards for real-world data: expert perspectives from a qualitative Delphi survey. JMIR medical informatics 2022; 10(1): e30363. http://dx.doi.org/10.2196/30363
- Ciampi F, Marzi G, Demi S and Faraoni M: The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives. Journal of Knowledge Management 2020; 24(5): 1157-1176. http://dx.doi.org/10.1108/JKM-02-2020-0156
- Galetsi P, Katsaliaki K and Kumar S: Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management 2020; 50: 206-216. https://doi.org/10.1016/j.ijinfomgt.2019.05.003
- Dash S, Shakyawar SK, Sharma M and Kaushik S: Big data in healthcare: management, analysis and future prospects. Journal of big data 2019; 6(1): 1-25. https://doi.org/10.1186/s40537-019-0217-0
- Chattu VK: A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health. Big Data and Cognitive Computing 2021; 5(3): 41. https://doi.org/10.3390/bdcc5030041
- Pappa D and Stergioulas LK: Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. International Journal of Data Science and Analytics 2019; 8(2), 113-135. https://doi.org/10.1007/s41060-019-00175-3
- Correia RB, Wood IB, Bollen J and Rocha LM: Mining social media data for biomedical signals and health-related behavior. Annual review of biomedical data science 2020; 3: 433-458. https://doi.org/10.1146/annurev-biodatasci-030320-040844
- Rodríguez-Sánchez JL, González-Torres T, Montero-Navarro A and Gallego-Losada R Investing time and resources for work–life balance: The effect on talent retention. International journal of environmental research and public health 2020; 17(6): 1920. https://doi.org/10.3390%2Fijerph17061920
- 23. Calina D, Hernández AF, Hartung T, Egorov AM, Izotov BN, Nikolouzakis TK, Tsatsakis A, Vlachoyiannopoulos PG and Docea AO: Challenges and scientific prospects of the newest generation of mRNA-based vaccines against SARS-CoV-2. Life 2021; 11(9), 907. https://doi.org/10.3390/life11090907
- Jovic D, Liang X, Zeng H, Lin L, Xu F and Luo Y: Single‐cell RNA sequencing technologies and applications: A brief overview. Clinical and Translational Medicine 2022; 12(3), e694. https://doi.org/10.1002/ctm2.694
- Stark R, Grzelak M and Hadfield J: RNA sequencing: the teenage years. Nature Reviews Genetics 2019; 20(11), 631-656. https://doi.org/10.1038/s41576-019-0150-2
- Leal F, Chis AE, Caton S, González-Vélez H, García-Gómez JM, Durá M, Sánchez- García A, Sáez C, Karageorgos A, Gerogiannis VC and Xenakis A: Smart pharmaceutical manufacturing: Ensuring end-to-end traceability and data integrity in medicine production. Big Data Research 2021; 24 100172. https://doi.org/10.1016/j.bdr.2020.100172
- Kavasidis I, Lallas E, Mountzouris G, Gerogiannis VC and Karageorgos A: A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes. IEEE Access 2023; 57585-57597. https://doi.org/10.1109/ACCESS.2023.3282316
- Žagar J and Mihelič J: Big data collection in pharmaceutical manufacturing and its use for product quality predictions. Scientific data 2022; 9(1): 99. https://doi.org/10.1038%2Fs41597-022-01203-x
- Mikulic M: Total global market for personalized medicine 2015-2022. Statista 2107; Available at: [https://www.statista.com/statistics/728124/global-market-for-personalized- medicine/ Accessed on: 25 October, 2023].
- Bleys J, Coravos J, Quigley D and Fleming E: A new portfolio model for biotech. McKinsey & Company 2021. Available at: [https://www.mckinsey.com/industries/life- sciences/our-insights/a-new-portfolio-model-for-biotech Accessed on: 27 October, 2023].
- Lu RM, Hwang YC, Liu IJ, Lee CC, Tsai HZ, Li HJ and Wu HC: Development of therapeutic antibodies for the treatment of diseases. Journal of biomedical science 2020; 27(1): 1-30. https://doi.org/10.1186/s12929-019-0592-z
- Hägg S, Jylhävä J, Wang Y, Czene K and Grassmann F: Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance. Human Genetics 2021; 140: 849-861. https://doi.org/10.1007/s00439-020-02249-w
- Moingeon P, Kuenemann M, and Guedj M: Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine. Drug discovery today 2022; 27(1): 215-222. https://doi.org/10.1016/j.drudis.2021.09.006
- Mak KK and Pichika MR: Artificial intelligence in drug development: present status and future prospects. Drug discovery today 2019; 24(3): 773-780.
- https://doi.org/10.1016/j.drudis.2018.11.014
- Malik S, Muhammad K and Waheed Y: Emerging applications of nanotechnology in healthcare and medicine. Molecules 2023; 28(18): p.6624. https://doi.org/10.3390%2Fmolecules28186624
- Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C and Tsaroucha AK: Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Current Oncology 2021; 28(3): 1581-1607. https://doi.org/10.3390/curroncol28030149
- Holst J: Global Health - emergence, hegemonic trends and biomedical reductionism. Global Health 2020; 16, 42. https://doi.org/10.1186/s12992-020-00573-4
How to cite this article:
Bana T, Kadu P and Pimple P: Navigating the future of medicine: exploring the latest breakthroughs in the pharmaceutical industry for a brighter tomorrow. Int J Pharm Sci & Res 2024; 15(7): 1991-99. doi: 10.13040/IJPSR.0975-8232.15(7).1991-99.
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1991-1999
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English
IJPSR
T. Bana, P. Kadu * and P. Pimple
Mrs. Saraswati Wani College of Pharmacy, Ganegaon, Ahmednagar, Maharashtra, India.
pramodkadu4@gmail.com
06 January 2024
13 February 2024
16 April 2024
10.13040/IJPSR.0975-8232.15(7).1991-99
01 July 2024