Emerging Challenges and Opportunities in Pharmaceutical Manufacturing and Distribution

A quest for stakeholder synchronization in the CAR T-cell therapy supply chain

Advancements in cell therapy have the potential to improve healthcare accessibility for eligible patients. However, there are still challenges in scaling production and reducing costs. These challenges involve various stakeholders such as the manufacturing facility, third-party logistics (3PL) company, and medical center. Proposed solutions tend to focus on individual companies rather than addressing the interconnectedness of the supply chain's challenges. The challenges can be categorized as barriers from product characteristics, regulatory requirements, or lagging infrastructure. Each barrier affects multiple stakeholders, especially during a boundary event like product handover. Therefore, solutions that only consider the objectives of one stakeholder fail to address underlying problems. This review examines the interconnecting cell therapy supply chain challenges and how they affect the multiple stakeholders involved. The authors consider whether proposed solutions impact individual stakeholders or the entire supply chain and discuss the benefits of stakeholder coordination-focused solutions such as integrated technologies and information tracking. The review highlights how coordination efforts allow for the implementation of widely-supported cell therapy supply solutions such as decentralized manufacturing through stakeholder collaboration.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era.

Design of a pilot plant-type pharmaceutical reactor to address the problem of interchangeability of generic semisolid formulations

OBJECTIVE AND SIGNIFICANCE

This research aims to design and develop a pilot plant-type pharmaceutical reactor with a strong focus on its volumetric capacity and heat transfer capabilities. The primary goal is to replicate design and control strategies at the laboratory or pilot scale to analyze and produce generic semisolid formulations.

METHODS

Computational fluid dynamics and heat transfer modeling, utilizing the finite volume method, were employed to determine the reactor's performance and particle trajectory during the mixing and stirring. This allowed for the establishment of optimal operational parameters and variables. Furthermore, prototypes were constructed at 1:2.5 and 1:15 scales to examine the reactor's morphology, ensure volumetric versatility, and conduct mixing, homogenization, and coloration tests using varying volumes.

RESULTS AND CONCLUSIONS

The outcomes of this study yielded a versatile reactor suitable for processing pharmaceutical semisolids at both laboratory and pilot-scale volumes. Notably, the reactor demonstrated exceptional volumetric capacity within a single vessel while effectively facilitating heat transfer to its interior.

Current and future prospective of pharmaceutical manufacturing in Saudi Arabia

This observational descriptive study that was carried out with the objective of exploring the contribution of the local pharmaceutical industry to the Saudi drug security. Using a drug formulary provided from the Saudi Food and Drug Authority, containing all registered pharmaceutical products available in Saudi Arabia, we extracted information about drug class, drug type, country and place of manufacturing, shelf-life and price. Results showed that the majority of drugs in the market are manufactured in Europe (43.86%), followed by Saudi Arabia (22.55%), China and India (20.47%), Americas (10.24%), and other nations (2.61%). Most of the manufactured drugs were prescription drugs (90.62%). In this work, the local pharmaceutical industry with the highest percentage of contribution to local drug security was Pharmaceutical Solution Industries (PSI), representing the 5% of the items available in the Saudi market. The second highest percentage was 4% by TABUK Pharmaceutical Manufacturing CO., followed by SPIMACO (3%), JAMJOOM pharmaceutical company (2%), Riyadh pharma (2%), and Jazeera pharmaceutical industries (2%). In addition, results from this study provide information about the most essential pharmaceutical products that needs to be nationally manufactured or increased in production in order to rise the contribution of local pharmaceutical industries in Saudi drug security. Unfortunately, the small contribution of the Saudi pharmaceutical industry in local drug security increases the burden on the Kingdom's annual budget due to the over-reliance on international pharmaceuticals.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO₂, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO₂SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Expanding the Horizons of Manufacturing, towards Wide Integration, Smart System, and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components [...]

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Digitalization in pharmaceutical industry: What to focus on under the digital implementation process?

Digitalization of any manufacture industry is a key step in any progress of the production process. The process of digitalization includes both increased use of robotics, automatization solutions and computerization, thereby allowing to reduce costs, to improve efficiency and productivity, and to be flexible to changes. Pharmaceutical Industry (PI) has however been resistant to digitalization, mainly due to fair experience and complexity of the entailed development and manufacture processes. Nevertheless, there is a clear need to digitalize PI as the demand in both traditional and new drugs is constantly growing. Contract Development Manufacture Organizations (CDMOs) have a special digitalizing challenge. Digitalization of PI, and CDMO precisely, should be tightly related to the main aspects of Good Manufacture Practice (GMP), and, to succeed in PI digitalizing requires constant focus on GMP. Close collaboration with constantly changing stakeholders is another important factor which should be in focus during digitalization of CDMO. This paper represents an overview over the main aspects of CDMO digitalization and discusses both the opportunities and challenges of the process, focusing on the practical solutions for successive digital implementation.

Optimal planning of the COVID-19 vaccine supply chain

This work presents a novel framework to simultaneously address the optimal planning of COVID-19 vaccine supply chains and the optimal planning of daily vaccinations in the available vaccination centres. A new mixed integer linear programming (MILP) model is developed to generate optimal decisions regarding the transferred quantities between locations, the inventory profiles of central hubs and vaccination centres and the daily vaccination plans in the vaccination centres of the supply chain network. Specific COVID-19 characteristics, such as special cold storage technologies, limited shelf-life of mRNA vaccines in refrigerated conditions and demanding vaccination targets under extreme time pressure, are aptly modelled. The goal of the model is the minimization of total costs, including storage and transportation costs, costs related to fleet and staff requirements, as well as, indirect costs imposed by wasted doses. A two-step decomposition strategy based on a divide-and-conquer and an aggregation approach is proposed for the solution of large-scale problems. The applicability and efficiency of the proposed optimization-based framework is illustrated on a study case that simulates the Greek nationwide vaccination program. Finally, a rolling horizon technique is employed to reactively deal with possible disturbances in the vaccination plans. The proposed mathematical framework facilitates the decision-making process in COVID-19 vaccine supply chains into minimizing the underlying costs and the number of doses lost. As a result, the efficiency of the distribution network is improved, thus assisting the mass vaccination campaigns against COVID-19.