1. Introduction
The pharmaceutical industry, one of the pillars of the modern economy and healthcare, is on the verge of profound structural changes. Traditional business models, based on the development of blockbusters and patent protection, are facing serious challenges due to the expiration of patent protection for key drugs (Indegene, 2025). This situation is exacerbated by the growing burden on healthcare systems, caused by the rapid aging of the population and the increasing prevalence of chronic diseases such as diabetes, obesity, and oncological pathologies (Coyle & Trikha, 2024). To overcome these obstacles, the industry is actively turning to innovation, restructuring its approaches to the development, production, and distribution of medicines.
The research problem is that the traditional model of drug development, characterized by high capital costs (over $2.6 billion per drug) and long timelines (from 10 to 15 years), is becoming inefficient and unsustainable. The average success rate for a drug reaching the approval stage is less than 10% (Paul et al., 2025). To maintain competitiveness and meet the growing needs in the healthcare sector, pharmaceutical companies are forced to implement breakthrough technologies. However, these changes are associated with new, often unpredictable, barriers and risks that require a comprehensive and in-depth understanding.
The scientific novelty of this work lies in the fact that it presents an original synthesis of data from various fields—market analysis, technological research, and regulatory reviews. Unlike highly specialized studies that focus only on individual aspects, this work describes key trends and establishes causal relationships between them. We identify systemic barriers that hinder the full implementation of innovations and provide a holistic picture of the industry’s transformation, which has both theoretical and practical value.
The objective of this study is to analyze the key trends that determine the development of the modern pharmaceutical market, to reveal their structural and technological features, and to identify the conditions for the successful integration of innovations into the business models and regulatory practices of the industry.
2. Materials and Methods
The methodological foundation of this study is formed at the intersection of pharmaceutical economics, digital transformation, and regulatory research, which is necessitated by the complex nature of the transformation of the global pharmaceutical market. The primary research tool is a qualitative content analysis of scientific, applied, and analytical literature, including articles in peer-reviewed journals, as well as industry reviews and consulting reports.
The study relies on sources covering both technological innovations and the institutional aspects of the industry’s development. Special attention is given to the work of Ferreira F. J. N. and Carneiro A. S., which presents a systematic review of the application of AI in drug development (Ferreira & Carneiro, 2025). The study by Membrane Technology examines the possibilities of reducing R&D timelines through the implementation of predictive models and machine learning algorithms (Paul et al., 2025).
For the analysis of the impact of personalized medicine, materials from Morsi M. H. et al. were used, which systematize the barriers and prospects for the implementation of precision therapy technologies (Morsi et al., 2025). This data is supplemented by the findings of Boudi A. L. and colleagues, who focus on the ethical challenges of AI in medicine (Boudi et al., 2024).
The digitalization of supply chains and the application of blockchain technologies were studied using the example of the work by Sim C., Zhang H., and Chang M. L., which examines the eZTracker system as a tool for end-to-end drug traceability (Sim et al., 2022). In a broader market context, the analysis was conducted based on consulting reviews by Coyle B. & Trikha K., which record the key strategic shifts in the pharmaceutical industry (Coyle & Trikha, 2024), and Indegene, which covers global trends in the life sciences (Indegene, 2025). Additionally, forecast models for growth and structural changes presented in analytical materials from Grand View Research, which record the dynamics of the small molecule and biopharmaceutical segments (Grand View Research, 2024), and Towards Healthcare, which offers scenarios up to 2034 (Towards Healthcare, 2025), were used. Statistical data on regional distribution and sales channels were compared with reports from Fortune Business Insights (Fortune Business Insights, 2025).
A systematic literature review and elements of content analysis, based on the recommendations of DDReg Pharma (DDReg Pharma, n.d.), were used as the methodological basis. This approach allowed for the integration of empirical data and theoretical concepts, forming a holistic methodological strategy.
The search was carried out in Scopus, Web of Science, PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar for the period from January 2019 to August 2025. The main queries combined terms such as “pharmaceutical market” or “life sciences industry” with “trend,” “outlook,” or “forecast”; “drug discovery” with “AI,” “machine learning,” “deep learning,” or “generative”; “companion diagnostic” or “CDx” with “FDA,” “EMA,” or “IVDR”; and “blockchain” with “traceability” or “supply chain.”
The review included peer-reviewed articles, regulatory documents (FDA/EMA), and market reports with transparent methodology. PR materials, blogs without methodology, and duplicates were excluded. Study selection followed the PRISMA approach. For grey literature sources (such as ZS, Indegene, Grand View Research, Towards Healthcare, Fortune Business Insights), the date of access, page version, and quality indicators—openness of models and data, and the presence of disclaimers, were recorded.
3. Results
The pharmaceutical market is demonstrating steady growth, despite global economic challenges. The expected compound annual growth rate (CAGR) for the period from 2025 to 2030 will be 6.1%. This will lead to an increase in the market volume to $2350429.1 million (Grand View Research, 2024). An analysis of the market structure reveals two key segments. Traditional drugs, or small molecules, maintain a dominant position, accounting for 57.56% of the global market in 2024, with total sales of $947.36 billion. However, the segment of biopharmaceuticals and biosimilars is showing the highest growth dynamics, making it the most profitable area (Grand View Research, 2024). The biosimilars market itself is showing impressive growth rates, which is due to the mass expiration of patents on expensive blockbuster drugs (Indegene, 2025). Regional dynamics are undergoing changes. North America remains the leader, generating 41.9% of the global sales revenue in 2024 (Grand View Research, 2024). Its dominance is supported by an advanced infrastructure, significant investments in R&D, and a high speed of new drug approvals. At the same time, rapid growth is being observed in the markets of the Asia-Pacific region and the Middle East/Africa (Towards Healthcare, 2025). Table 1 shows the forecast for the growth of the global pharmaceutical market by region, which clearly illustrates the shift in global growth centers.
Table 1. Forecast of global pharmaceutical market growth by region.
Regional baseline levels and trajectories were taken from Towards Healthcare (horizon to 2034) and Grand View Research (to 2030), which differ in time horizons, segmentation (small molecules vs. biologics/biosimilars), and channel coverage (including online pharmacies). As a result, the CAGR spread of ~6.1% - 7.5% is observed across different sections. This range is interpreted as a consequence of differences in scope, time horizons, and providers’ model assumptions. For a “frame” validation of upper and lower bounds, data from Fortune Business Insights were used. Where approximation plateaus or repeated intermediate values appear in tables, we mark them as model artifacts and consider only the direction of change (Grand View Research, 2024; Towards Healthcare, 2025; Fortune Business Insights, 2025).
Artificial intelligence is radically changing the approach to drug development, shifting it from the realm of lengthy and costly laboratory experiments to the sphere of predictive analysis and design. AI reduces the time required to bring a drug to market and lowers the financial costs of preclinical and clinical trials. According to research, AI can reduce the timelines of the early R&D stage by up to 50% (Paul et al., 2025). Peer-reviewed reviews show that AI provides measurable gains in hit/lead quality and prioritization, reduces the number of wet-lab iterations, and helps optimize protocol design in early clinical stages. However, the magnitude of the effect varies across tasks and datasets, and universal percentages are inappropriate without replicable studies. In this work, we replace the generalized “up to 50%” formula with qualified statements grounded in peer-reviewed reviews and case studies, while reporting specific percentages only where open and reproducible empirical data are available (Ferreira & Carneiro, 2025; Paul et al., 2025). This is achieved through the automation and optimization of processes at all stages of development. The application of AI extends to various stages of pharmaceutical R&D: target identification (AI algorithms, including graph neural networks (GNNs) and transformer architectures, analyze vast amounts of genomic, proteomic, and other biological data to identify new therapeutic targets (Ferreira & Carneiro, 2025), compound optimization (Generative AI designs new molecules with predefined properties (Morsi et al., 2025), and clinical trials (AI optimizes protocol design, using predictive analytics to improve the accuracy of participant selection (Fortune Business Insights, 2025). Table 2 presents the integration of AI into the drug development process.
Table 2. Integration of AI into the drug development process.
R&D Stage |
AI Application |
Result |
Sources |
Target Identification |
Analysis of genomic, proteomic, and biological data; application of GNNs, NLP |
Discovery of new, previously unrecognized targets. |
(Ferreira & Carneiro, 2025) |
Compound Screening & Optimization |
Predictive screening; generative molecule design; use of generative AI |
Reduction of development time (up to 50%) and cost savings. |
(Paul et al., 2025) |
Preclinical Studies |
Prediction of toxicity and pharmacokinetics;
meta-learning-based modeling |
Improved efficiency and accuracy. |
(Paul et al., 2025) |
Clinical Trials |
Protocol design optimization; patient selection; analysis of EHR, RWE |
Reduced recruitment time and cost, lower dropout rates. |
(Paul et al., 2025) |
Personalized medicine, closely linked to AI technologies, is showing steady growth. The basis of this growth is advancements in genomics, artificial intelligence, and big data analysis. Next-generation sequencing-based testing allows for the rapid analysis of significant portions of the human genome, identifying genetic variations that affect disease predisposition and treatment response (Boudi et al., 2024). A key element in this process is companion diagnostics. These tests allow for the identification of patients who are most likely to benefit from a specific therapy, or those who are at high risk of serious side effects. Companies are transitioning from linear supply chains to flexible, interconnected digital supply networks. At the core of these networks are advanced technologies such as artificial intelligence, the Internet of Things (IoT), and digital twins, which provide real-time visibility and traceability. One of the key applications of digitalization is the fight against counterfeit products.
Blockchain technology creates a decentralized, immutable ledger that provides end-to-end product traceability from the manufacturer to the end consumer (Coyle & Trikha, 2024). This allows for effectively combating the spread of fake medicines, which cause economic damage and pose a direct threat to the health and lives of patients. A striking example is the eZTracker case study. Case selection and transferability of results. eZTracker was chosen as a documented multilateral pilot addressing end-to-end authentication and accelerated recalls in the Asian region. Although a single case study has limitations for external validity, its mechanisms—serialization, a permissioned ledger, QR/2D-code verification, and deviation alerts—align with standard pharmaceutical traceability requirements (GxP, anti-counterfeit). Therefore, they are transferable under comparable labeling standards and data-sharing agreements (Sim, Zhang, & Chang, 2022). After incidents with counterfeit HPV vaccines in Hong Kong (China) and fake fillers in Thailand in 2019, several pharmaceutical companies deployed the eZTracker blockchain project (Sim, Zhang, & Chang, 2022). This system allows patients and healthcare professionals to verify the authenticity of a drug in real time by scanning a QR code on the packaging. Unauthorized scans or movements of the product immediately trigger an alert in the system, which allows for a prompt response to suspicious activity. Another critical element is smart logistics, especially in the management of the “cold chain”. With the growing share of biopharmaceuticals, vaccines, and gene therapies that are sensitive to temperature, maintaining strict storage and transportation conditions is vital. IoT sensors integrated into packaging continuously monitor temperature, humidity, and location, transmitting data to cloud systems in real time. This ensures the quality and safety of the product at all stages of delivery and allows for a prompt response to any deviations (DDReg Pharma, n.d.). Table 3 examines the workflow of a drug tracking system.
The presented scheme demonstrates how blockchain technology provides end-to-end transparency and security in the pharmaceutical supply chain. At the manufacturing stage, each product is assigned a unique encrypted digital identifier, which is recorded on the blockchain. All subsequent operations—from distribution to sale—are recorded in the system, which minimizes the risk of counterfeit drugs entering the market. Scanning 2D or QR codes allows distributors and pharmacies to confirm the authenticity of products, and patients to independently verify their origin and integrity. This approach strengthens the trust of market participants and promotes compliance with regulatory requirements for drug tracking.
Table 3. Workflow of a blockchain-based drug tracking system.
Stage |
Participant |
Action/Technology |
Sources |
Manufacturing |
Manufacturer |
Product serialization, uploading of encrypted digital ID to blockchain. |
(Sim, Zhang, & Chang, 2022) |
Distribution |
Distributor |
2D code scanning, creation of a unique ID in the blockchain. |
(Sim, Zhang, & Chang, 2022) |
Sale |
Pharmacy/Hospital |
Transfer of the product to the end consumer. |
(Sim, Zhang, & Chang, 2022) |
Verification |
Patient |
QR code scanning to verify authenticity and origin. |
(Sim, Zhang, & Chang, 2022) |
4. Discussion
The differences in market forecasts presented by various consulting companies are not a contradiction, but rather reflect the deep complexity and multifactorial nature of the pharmaceutical market. For example, the forecast CAGR values range from 6.1% to 7.5%. These discrepancies may be caused by different methodologies for data collection and analysis, the inclusion or exclusion of certain market sub-segments, and differences in the forecast horizon (e.g., up to 2030 versus 2034) (Grand View Research, 2024). Presenting a range of forecast values instead of a single “true” figure is a more academically sound approach, which indicates the high degree of uncertainty inherent in dynamic markets.
The implementation of AI signifies a fundamental shift in the R&D paradigm. The traditional approach, based on the manual testing of millions of compounds through “trial and error”, has a high failure rate and leads to enormous financial and time losses (Paul et al., 2025). In contrast, AI allows the focus to be shifted to “prediction and design”. Instead of randomness and mass screening, companies use AI for predictive modeling, which predicts the properties of molecules and their interaction with biological targets with high accuracy. This does not just “accelerate” old processes but completely rethinks them, leading to a radical increase in productivity and a change in the economics of pharmaceutical R&D.
The transition to personalized medicine is not just a technological trend but a systemic transformation of the entire healthcare ecosystem. The development of targeted therapies and diagnostic tests requires the creation of new drugs and a complete restructuring of the entire infrastructure, from regulatory norms to clinical practice (Boudi et al., 2024). Existing regulatory frameworks were created for traditional methods and do not account for the complexities of new technologies, such as NGS testing, which creates barriers to their widespread adoption (Morsi et al., 2025). This transition requires new approaches to educating physicians and raising patient awareness, especially regarding genetic risks and data privacy.
The implementation of blockchain is not just a step towards automation but a strategic initiative aimed at increasing trust in the industry and ensuring public safety. The technology allows the function of authenticity control to be transferred to the end consumer, which is a direct response to the dual challenge—economic and social—posed by the problem of counterfeiting. Despite the enormous potential, the implementation of new technologies faces serious obstacles that slow the pace of transformation. These barriers are complex in nature and cover the regulatory, ethical, and operational spheres.
Existing regulatory frameworks, designed for traditional processes, cannot keep up with the pace of technological progress. The absence of standardized approaches to the validation of AI models and new types of diagnostics, such as NGS testing, creates uncertainty for developers (Fortune Business Insights, 2025). Furthermore, the lack of unified international standards complicates entry into global markets. Regulatory agencies, including the U.S. Food and Drug Administration (FDA), are still in the process of developing guidelines for AI applications in pharmaceuticals.
The use of vast amounts of data (genomic, medical) creates risks of leaks and unauthorized access (Indegene, 2025). A striking example is the data breach at the genetic company 23andMe in 2023, which compromised the information of 6.9 million users (Boudi et al., 2024). To mitigate these risks, it is necessary to implement strict protocols, such as data minimization and “privacy by design”, and to comply with the requirements of regulatory acts like HIPAA and GDPR.10 AI models trained on non-representative datasets can reproduce and even exacerbate existing health inequalities. For example, algorithms in dermatology trained predominantly on data from light-skinned patients show low accuracy on people with dark skin, which can lead to delays in diagnosis and treatment. Many complex AI models are opaque. The inability to understand how a system arrived at a particular conclusion creates serious risks for patient safety, especially when making critical decisions in clinical practice.
The implementation of digital technologies faces operational obstacles. In the pharmaceutical industry, there has traditionally been a significant gap between information technology and operational technology, especially at manufacturing sites. This gap complicates system integration. The problem is exacerbated by the presence of legacy systems that do not support modern data protocols (Fortune Business Insights, 2025). Moreover, the pharmaceutical industry is traditionally conservative and slow to adapt to change. The implementation of new solutions requires technological changes and a transformation of corporate culture, as well as the presence of qualified specialists, who are often in short supply.
The analysis shows that the key barriers to digital transformation lie not so much in the technological sphere as in the “soft” infrastructure—people, processes, and regulatory and ethical norms. The speed of adaptation of technological frameworks and human capital lags significantly behind the pace of technological progress itself. This means that for a successful digital transformation, companies and regulators need to shift their focus from the simple implementation of individual technologies to a comprehensive rethinking of operational processes and organizational structure, and to the development of flexible regulatory and ethical standards, which will ultimately ensure resilience and safety in the face of rapid change.
This analysis combines peer-reviewed studies with industry and consulting sources (ZS, Indegene, Grand View Research, Towards Healthcare, Fortune Business Insights). The latter provide timely market coverage but disclose their models and assumptions only partially, which adds uncertainty to forecasts. The reported ranges for market size and CAGR reflect both genuine scenario variability and methodological differences among providers. Quantitative claims about AI-driven acceleration of R&D should be interpreted with caution and, where possible, triangulated with regulatory documentation and reproducible case studies (Coyle & Trikha, 2024).
5. Conclusion
The study conducted has allowed for the systematization and theoretical conceptualization of the modern trends shaping the development of the global pharmaceutical market, the identification of their structural determinants, and the outlining of directions for further transformation in the context of technological and institutional turbulence. It has been established that the industry’s dynamics are determined by the simultaneous impact of macroeconomic factors, demographic pressure, and technological breakthroughs, among which artificial intelligence, personalized medicine, and the digitalization of supply chains play a key role.
A comprehensive analysis has shown that the integration of AI into R&D processes provides a transition from the empirical “trial and error” paradigm to a model of prediction and design, radically increasing the efficiency of drug development. The development of personalized medicine necessitates a restructuring of the healthcare infrastructure and regulatory mechanisms, forming new standards for diagnosis and therapy. Digital technologies and blockchain are transforming the system of logistics and authenticity control, enhancing the transparency and resilience of pharmaceutical supply chains.
Special attention in the work was given to identifying the barriers to the implementation of innovations, including institutional constraints, regulatory uncertainty, cybersecurity risks, and socio-ethical dilemmas. The importance of developing flexible normative and organizational models, oriented towards a balance between the speed of technological adaptation and patient safety, has been emphasized.
Thus, the presented conceptual model demonstrates that the future of the pharmaceutical industry is determined by the speed of implementation of advanced technologies and the ability to build a sustainable system of change management based on a partnership between the state, business, and society. Prospects for further research are related to a deeper analysis of the mechanisms of global integration of innovations, the modeling of digital transformation scenarios, and the development of strategies that ensure competitiveness and public safety in the context of accelerating global transformations.