Innovations in Pharmaceutical R&D: Navigating the Future.
December 06, 2023
Welcome readers, to our esteemed panel discussion on ‘Innovations in Pharmaceutical R&D: Navigating the
Future.’ We are honored to have a distinguished group of experts who bring a wealth of experience and insights from various facets of the pharmaceutical industry.
Question 1. As we see an increasing emphasis on data-driven decision-making, can you discuss specific examples of how big data analytics and computational modeling are being applied to streamline and enhance various stages of the drug development lifecycle?
LAKSHMI RAGHAVAN: Data-driven decisionmaking is becoming mainstream in various stages of drug development and is transforming the drug development lifecycle, streamlining processes, reducing costs and timelines, and improving the probability of success. Critical to different stages of drug development are data analytics and is primarily dependent on how good the data are. Given below are some examples of different stages of drug development lifecycle where data analytics and computational modeling are being applied to streamline and enhance various stages of the drug development lifecycle.
1. Target Identification and Validation: There are vast amounts of genomic, proteomic, and phenotypic data available, which are mined and analyzed to identify new potential therapeutic targets. These targets are validated by analyzing data from animal models, clinical trials and real world evidence for the drug target’s safety and efficacy.
2. Drug Discovery and Development: A majority of data analytics in the drug development lifecycle is focused on in the area of drug discovery and development as small and large pharmaceutical companies are find that data analytics and computational modeling can significantly reduce the time and resources need with conventional drug discovery process. For example, machine language algorithms are used to predict the properties of the drug candidates such as pharmacokinetics and pharmacodynamics. Similarly, drugs designs are optimized by analyzing molecular data to identify compounds with desirable properties and how they interact with biological targets.
3. Clinical Trials: Several companies are engaged in using data analytics to shorten the clinical study timelines, reduce costs as machine language can be used to generate predictive models that can accurately predict the clinical study outcome. In addition, data analytics are being used in patient selection by identifying patients who are likely to respond to a particular treatment. Optimization of clinical trial design by identifying the most informative endpoints, selecting appropriate patient populations and determining the optimal dosing regimens is another area of focus in the world of data analytics. Lastly, real-time monitoring of clinical trials data to identify potential safety concerns and optimize clinical trial outcomes is used to significantly reduce the risk, cost and time.
4. Post-Market Surveillance: Data analytics is used effectively in identifying adverse drug reactions (ADR) that are not reported through traditional methods by analyzing data from social media, electronic health records (EHRs) and insurance claims. Analysis of data from large patient’s populations can help monitor the safety and efficacy of the drugs in real world settings. Another area where data analytics are being explored are in personalized medicine/ Pharmaceutical companies are continually finding new ways to use data analytics and computational modeling to make more informed decisions at every stages of the drug development lifecycle, paving the way to bring safer and effective drugs to market
quicker and at lower cost.
Question 2. Adaptive clinical trials are gaining attention for their potential to optimize trial designs in real-time. How do you see this approach impacting the efficiency and success rates of clinical trials, and what challenges need to be addressed for widespread adoption?
SOMESH SHARMA: Adaptive clinical trials as the name indicates is all about flexibility, nimbleness and innovative clinical trial approach, for prospective planned modifications based on interim data analysis from subjects in trial. Adaptive clinical designs offer enormous benefits over traditional fixed design plan when information is limited before beginning of trial.
The main advantages fall under following categories as outlined in FDA guidelines –
i) Statistical efficiency: An adaptive design can provide a greater chance to detect a true drug effect than non-adaptive design in group sequential designs, and adaptive modifications to the sample size.
ii) Ethical considerations: Discontinue or stop a trial early if interventions reflect ineffectiveness of investigational treatment, and in course helps to reduce the number of patients exposed to the unnecessary risk.
iii) Improved understanding of drug effects: An adaptive design strategy enriches the information from small population of patients and estimates better understanding of experimental treatment.
iv) Acceptability to stakeholders: An adaptive design is more amenable to stakeholders as it allows planned design modifications based on accumulating information.
Adaptive design approach can enhance the chance of successful trials and translation of information across all phases of clinical development – from dose finding (Phase-1) to confirmatory Phase III trials. However, all of this presents some difficulties that must be thought out and considered before being widely adopted. The adaptive design method results in longer trial start duration and more work during the design phase. There aren’t enough particular,user-friendly statistical tools to prevent bias and incorrect results. In comparison to a non-adaptive design, an adaptive design may have a larger maximum sample size but a smaller minimum and predicted sample size. Furthermore, it is difficult to guarantee the timely availability of high-quality interim data, moreover, important scientific limitations or specific clinical contexts may restrict the potential for efficiency through adaptation. To sum up, adaptive clinical trials can optimise trial designs in real time, which would increase trial efficiency and appeal to enrolled participants. But before adaptive clinical trials become widely used, several issues must be resolved. These issues include the need for regulatory guidelines, a clear statistical approach, and a greater comprehension of the ethical implications of adaptive clinical trials.
Question 3. The shift towards patientcentric approaches is transforming the traditional R&D paradigm. How can pharmaceutical companies effectively incorporate patient insights and experiences into the drug development process to ensure more targeted and successful outcomes?
SHAMAL FERNANDO:Incorporating patient insights and experiences into the drug development process is crucial for pharmaceutical companies to enhance the effectiveness and relevance of their products. Here are several strategies that companies can employ to effectively integrate patient-centric approaches into the R&D paradigm:
Early and Continuous Patient Engagement:
Involve patients from the early stages of drug development, including protocol design, endpoint selection, and study planning. Establish ongoing communication channels to ensure continuous engagement throughout the research process.
Patient Advisory Boards:
Form patient advisory boards comprising individuals with the target condition to provide input on various aspects of the drug development process. Seek input on study design, recruitment strategies, and patient reported outcomes.
Patient-Reported Outcomes (PROs):
Incorporate patient-reported outcomes into clinical trial designs to capture the impact of the disease and treatment from the patient’s perspective. Use validated instruments to measure symptoms, daily functioning, and quality of life directly from the patient’s point of view.
Real-World Evidence (RWE):
Leverage real-world evidence from sources such as electronic health records, wearables, and patient registries to complement traditional clinical trial data. Gain insights into the long-term safety and effectiveness of treatments in real-world settings.
Digital Health Technologies:
Explore digital tools, such as mobile apps and wearables, to collect real-time data on patient experiences, adherence, and outcomes. Use technology to facilitate remote monitoring and data collection, reducing the burden on patients.
Education and Empowerment:
Educate patients about the drug development process, clinical trials, and the importance of their participation. Empower patients to actively contribute by fostering a collaborative relationship between researchers and patients.
Diversity and Inclusion:
Ensure diversity in patient populations participating in clinical trials to reflect the broader patient demographics. Consider
cultural and socioeconomic factors that may influence patient experiences and preferences.
Collaborate with regulatory agencies to incorporate patient perspectives into the regulatory decision-making process.
Understand and align with evolving regulatory guidelines that emphasize patient engagement.
Data Transparency and Communication:
Share study results with patients in a clear and accessible manner. Foster open communication to build trust and demonstrate a commitment to transparency.
Partnerships and Collaborations:
Collaborate with patient advocacy groups, non-profit organizations, and other stakeholders to access a diverse range of patient perspectives.
Establish partnerships to co-create research agendas and prioritize areas of focus.
By implementing these strategies, pharmaceutical companies can enhance the patient-centricity of their drug development processes, leading to more targeted, relevant, and successful outcomes. This approach not only aligns with ethical considerations but also contributes to the development of therapies that better meet the needs and preferences of the individuals they are intended to benefit.
Question 4. Artificial intelligence is increasingly being utilized for drug repurposing. In your view, what role does AI play in identifying new therapeutic uses for existing drugs, and how does this contribute to the acceleration of drug development timelines?
SOMESH SHARMA: The process of finding novel therapeutic applications for already-approved medications is known as “drug repurposing,” and artificial intelligence (AI) tools are becoming more and more significant in this regard. AI systems have the capacity to evaluate humongous data and spot trends that humans might miss, which speeds up and simplifies the process of discovering new therapeutic uses for existing drugs more quickly and efficiently.
Repurposing medications is a low-risk approach since it involves less financial commitment, fewer unknowns, less undesirable side effects than developing new drugs as the drugs to be examined have already been approved. A few successful examples of medications that have been repurposed in the past include Rituximab, which was first used to treat cancer but has shown to be effective in treating rheumatoid arthritis, and Sildenafil, which was first developed as an antihypertensive medication and later proved to be effective in treating erectile dysfunction. Even during COVID-19 pandemic, already approved drugs like Remdesivir (a drug for treating Ebola virus disease), Ivermectin (anthelmintic drug), Dexamethasone (anti-inflammatory drugs) are being studied for their efficacy against the disease and proved quite beneficial. The main advantage of drug repurposing is to reduce time and cost in developing new drugs and lower the safety risk assessment of new medications.
Machine learning and Deep learning tools can integrate heterogeneous data and predict drug-drug and drug-target interactions. As these tools become more predictive and authentic on availability of substantial pre-clinical and clinical data, and guide us for a synergistic drug combination for a disease, it will definitely reduce the overall drug development cost.
SHAMAL FERNANDO: Artificial intelligence (AI) plays a pivotal role in drug repurposing, contributing to the acceleration of drug development timelines by leveraging advanced computational techniques and data analytics.
Here are key aspects highlighting the role of AI in identifying new therapeutic uses for existing drugs:
Data Integration and Mining:
Diverse Data Sources: AI algorithms can efficiently integrate and analyze vast amounts of diverse data, including biomedical literature, electronic health records, genomics data, and drug databases.
Pattern Recognition: AI systems excel at recognizing complex patterns and relationships within this diverse data landscape, helping identify potential connections between drugs and diseases.
Machine Learning Algorithms: AI employs machine learning algorithms to build predictive models based on historical data. These models can predict potential drug-disease associations by identifying hidden patterns and correlations.
Feature Extraction: AI algorithms can extract relevant features from various data types, providing insights into the biological mechanisms underlying diseases and potential drug actions.
Systems-Level Analysis: AI facilitates network pharmacology approaches, where drugs, proteins, and diseases are considered as interconnected components in biological networks.
Identifying Targets: AI helps identify new therapeutic targets by analyzing the relationships between drug targets and diseaserelatedproteins.
High-throughput Screening and Virtual Screening:
Computational Screening: AI enables virtual screening of large chemical libraries to identify potential drug candidates for a specific disease.
Prioritization of Compounds: AIalgorithms can prioritize existing drugs based on their potential efficacy for a new therapeutic indication, reducing the time and cost associated with experimental screening.
Personalized Medicine and Biomarker Discovery:
Patient Stratification: AI contributes to the identification of patient subgroups that may respond differently to existing drugs, supporting the development of personalized treatment strategies.
Biomarker Discovery: AI algorithms can identify potential biomarkers associated with drug response or disease progression, aiding in the selection of appropriate patient populations for repurposing efforts.
Drug Safety and Toxicity Prediction:
Risk Assessment: AI models can predict potential safety concerns and toxicities associated with repurposed drugs, guiding decision-making in the drug development process.
Optimizing Formulations: AI can assist in optimizing drug formulations to enhance safety profiles and reduce adverse effects.
Rapid Hypothesis Generation:
Iterative Learning: AI enables an iterative and learning process, allowing researchers to rapidly generate and test hypotheses regarding potential drug-disease relationships.
Reduced Trial and Error: By providing data-driven insights, AI minimizes the need for extensive trial and error in the drug repurposing process.
Cost and Time Savings: AI-driven drug repurposing can significantly reduce the time and cost associated with the early stages of drug development.
Focused Experimental Validation: AI helps prioritize candidates for experimental validation, directing resources towards the most promising repurposing opportunities. In summary, AI expedites drug repurposing by leveraging its capacity for data integration, predictive modeling, and advanced analytics. By accelerating the identification of new therapeutic uses for existing drugs, AI contributes to more efficient drug development timelines, enabling the exploration of novel indications and potential treatments in a cost-effective manner.
Question 5. In light of technological advancements, regulatory agencies are evolving their approaches. How do you see innovations like real-world evidence, patient-reported outcomes, and digital biomarkers influencing the regulatory landscape, and what adaptations should the industry make to align with these changes?
MICHAEL N. LIEBMAN: I see challenges that face regulatory agencies in dealing with rapid technology development and have some concerns that the tendency is to rely, at least initially, on using the current standard of care as the gold standard. This can impact new technology that may be generating data and/or measuring parameters that are not just incremental improvements over existing methods. For example, continuous blood pressure monitoring that uses non-auscultatory methods should be evaluated based on what the new data may represent and whether the existing methods should be considered the gold standard because of inconsistent lack of clinical adherence to existing ciinical guidelines and whether point in time measures adequately describe the full circadian pattern of blood pressure variation. It is also incumbent on the industry, however, to design studies to appropriately show the value of such new technologies as to how they will improve patient care.
Question 6. Collaboration between pharmaceutical companies and technology firms is becoming more prevalent. Can you share insights on successful collaborations you’ve witnessed or been a part of, and how these partnerships are driving innovation in R&D processes?
Lakshmi Raghavan: While pharmaceutical companies are strong in developing drugs and coming out with safe and effective drugs, they rely on technology companies to provide insights on how data can be utilized and analyzed effectively to make the drug development process more efficient and reduce the risks, costs, and timeline. There are a few examples where pharmaceutical companies have collaborated with Technology companies driving innovations in research & development. Some of these collaborations are given below.
Boehringer Ingelheim and Google:
Boehringer Ingelheim (BI) formed a collaborative partnership with Google Quantum AI, focusing on researching and implementing cutting edge use cases for quantum computing in pharmaceutical research & development. The partnership was formed with the expertise of BI’s expertise in computer aided design and in silico modeling combined with Google’s outstanding resources in the field of quantum computing and algorithms. Quantum computing is expected to have the potential to accurately simulate and compare much bigger molecules than currently possible with conventional computing, and thus can create opportunities for newer therapies and treatments, which otherwise is difficult to treat.
AstraZeneca and IBM:
AstraZeneca’s Bioventure Hub and IBM, Sweden formed a collaborative partnership primarily to drive innovation through simulating growth of Small and Medium Enterprises (SMEs), support knowledge exchange between life sciences and digital technology industries and strengthen digital health expertise. AstraZeneca’s aspire to embrace data science, AI and Machine Language technologies to advance its fundamental understanding of the diseases, increase productivity and get faster access to medicines.
Roche and Flatiron Health:
Roche acquired Flatiron Health, which is a Healthtech company that is a market leader in oncology specific electronic health record software as we as in the curation and development of real-world evidence for cancer research. Flatiron Health is an independent affiliate of Roche and with their respective strengths in the oncology space, the digital technology solutions from Flatiron is expected to hasten Roche’s drug development in the
oncology space. There are several other pharmaceutical and technology partnerships that are evolving and changing the way the drug development process is understood and developed. They are primarily based on either corporate level strategy or product level strategy. There are also several other startups that are collaborating with other startups, providing an avenue for large pharma companies to test some of these concepts, which are more difficult to justify pursuing in-house.
Question 7. With the integration of advanced technologies, ethical considerations become paramount. How should the pharmaceutical industry navigate ethical challenges associated with innovations such as AI, CRISPR, and personalized medicine, and what frameworks can ensure responsible and transparent R&D practices?
SOMESH SHARMA: First of all, a paradigm shift in healthcare is being made possible through modern technical platforms like AI, CRISPR, and personalized medicine. As pharmaceutical industry envisages to integrate these technologies in their research and development practice, the ethical considerations of these tools have a propensity to draw attention due to lack of framework to ensure transparent, responsible practices and accountability of these technologies. Though, AI has revolutionised the medical field, viz. imaging and electronic medical records, laboratory diagnosis, robotic surgeries, treatment, new drug discovery, repurposing of existing medicine, preventive and precision medicine, biological extensive data analysis, data storage and access for health organizations. It’s interesting to note that it faces ethical and legal issues because of existing legislation is insufficient to protect people’s health records, a could lead to an unchecked and inaccurate flow of information on social networks, endangering people’s security and privacy. Similar questions are being raised about personalised medicines about privacy, safety, phenotypical expression, drug interactions, and genetic vs. social group identities and fairness in subject selection. In addition, personalized medicine will change the economics of drug production and distribution. CRISPR is a novel gene editing tool for treatment of cancer and many other diseases. Recent success in treatment of sickle cell anaemia in a patient has opened up a new horizon in healthcare. Interestingly, the use of genome editing for research and commercial purposes has too sparked debates on the effects of human genome editing on the patients themselves, and for future generation. The manipulation of these techniques can bring imbalance in social-economic behaviour of society. To ensure data privacy and protection, pharmaceutical industries can adopt responsible research innovation and ethics assessment framework to demonstrate socially responsible and ethical practices. These frameworks should provide guidelines and checkpoints to ensure transparency in data sharing and the ethical implications of these technologies for betterment of human society and future generations. General data protection regulation and informed consent and autonomy are steps towards privacy protection and ethical disclosures.
SHAMAL FERNANDO: The integration of advanced technologies in the pharmaceutical industry, such as AI, CRISPR, and personalized medicine, raises significant ethical considerations. Navigating these challenges requires a commitment to responsible and transparent research and development (R&D) practices. Here are key considerations and frameworks that can help the pharmaceutical industry address ethical challenges:
Informed Consent and Autonomy:
Framework: Implement robust informed consent processes, ensuring that patients and research participants fully understand the implications of innovative technologies and personalized treatments.
Consideration: Respect individual autonomy and provide clear information about the potential risks, benefits, and implications of participating in studies involving advanced technologies.
Data Privacy and Security:
Framework: Adhere to stringent data privacy and security standards to protect patient information in the era of personalized medicine and AI-driven analytics.
Consideration: Clearly communicate data usage policies, provide options for data sharing consent, and take measures to prevent unauthorized access.
Framework: Develop strategies to ensure equitable access to innovative treatments, considering factors such as affordability, geographic location, and socioeconomic status.
Consideration: Address potential disparities in access to advanced technologies and treatments, working towards inclusive and affordable healthcare solutions.
Transparency and Accountability:
Framework: Establish transparent communication channels with stakeholders, including patients, healthcare professionals, and regulatory bodies.
Consideration: Clearly communicate the goals, methods, and potential risks of R&D involving advanced technologies, and hold organizations accountable for ethical conduct.
Bias and Fairness:
Framework: Mitigate biases in algorithms used in AI applications by regularly auditing and validating these systems.
Consideration: Ensure that personalized medicine approaches do not inadvertently reinforce existing health disparities, and strive for fairness in treatment outcomes across diverse populations.
Genetic Privacy and Consent:
Framework: Develop clear guidelines for the ethical use of genetic information, including informed consent processes for genetic testing and sharing of genetic data.
Consideration: Safeguard against potential misuse of genetic information, including unauthorized access or discrimination based on genetic predispositions.
Frameworks for Responsible R&D Practices:
Ethics Review Boards (ERBs):
Role: Establish and strengthen ERBs to independently review and approve research protocols involving advanced technologies.
Consideration: Ensure ERBs have diverse expertise and include members knowledgeable about the specific ethical implications of AI,CRISPR, and personalized medicine.
Guidelines and Standards:
Role: Develop and adhere to industry-wide guidelines and standards for ethical conduct in R&D.
Consideration: Regularly update guidelines to reflect evolving ethical challenges associated with emerging technologies.
Role: Encourage international collaboration and harmonization of ethical standards to ensure consistency across borders.
Consideration: Navigate global regulatory frameworks and respect cultural differences while upholding fundamental ethical principles.
Role: Actively engage with patients, advocacy groups, healthcare professionals, and the public to gather diverse perspectives.
Consideration: Solicit input in the development of ethical guidelines, R&D priorities, and dissemination of research findings.
Continuous Ethical Training:
Role: Provide ongoing ethical training for researchers, clinicians, and other professionals involved in R&D.
Consideration: Keep individuals abreast of the latest ethical considerations associated with advanced technologies and personalized medicine.
Ethical Impact Assessments:
Role: Conduct thorough ethical impact assessments before initiating research projects involving advanced technologies.
Consideration: Evaluate potential social, cultural, and economic implications of the research, and address any identified ethical concerns proactively.
By adopting these frameworks and considerations, the pharmaceutical industry can navigate the ethical challenges associated with innovations such as AI, CRISPR, and personalized medicine. This approach helps to build trust among stakeholders, ensures responsible conduct in R&D, and ultimately contributes to the ethical advancement of healthcare technologies.
Question 8. Digital therapeutics are gaining traction as standalone treatments. How do you see the integration of digital therapeutics with traditional pharmaceuticals, and what opportunities and challenges does this convergence present for R&D strategies?
MICHAEL N. LIEBMAN: The fundamental challenge will be the need to shift the emphasis on the technology being developed to an emphasis on what is the critical problem that needs to be addressed. This aligns with the concept of patient-centered research and development where identification of the problem and potential root cause analysis is applied. This could help shift some of the focus from the current paradigm of “treating a disease” to also integrate additional prevention measures that the combination of digital therapeutics plus traditional pharmaceuticals could uniquely provide.
Question 9. Quantum computing holds promise for solving complex problems in drug discovery. How might quantum computing reshape the computational aspects of pharmaceutical R&D, and what potential breakthroughs can we anticipate in drug design and optimization?
SHAMAL FERNANDO: Quantum computing has the potential to revolutionize the computational aspects of pharmaceutical research and development (R&D) by addressing complex problems that classical computers struggle to solve efficiently. Here are several ways in which quantum computing might reshape pharmaceutical R&D and potential breakthroughs in drug design and optimization:
1. Molecular Simulation and Drug Discovery:
Current Challenge: Classical computers often face challenges simulating the quantum behavior of molecules accurately, limiting their ability to model complex biochemical interactions.
Quantum Impact: Quantum computers excel at simulating quantum systems, enabling more accurate modeling of molecular structures and interactions.
Breakthrough: Improved molecular simulations could lead to more precise predictions of drug behavior, efficacy, and potential side effects, expediting the drug discovery process.
2. Optimization Problems:
Current Challenge: Drug design involves solving complex optimization problems, such as finding the optimal molecular structure for a desired therapeutic effect.
Quantum Impact: Quantum algorithms, such as quantum annealing, have the potential to solve optimization problems exponentially faster than classical algorithms.
Breakthrough: Accelerated optimization could lead to the discovery of novel drug candidates with improved potency, selectivity, and reduced side effects.
3. Quantum Machine Learning:
Current Challenge: Classical machine learning approaches play a crucial role in drug discovery, but some problems, such as analyzing high dimensional datasets, can be computationally intensive.
Quantum Impact: Quantum machine learning algorithms, such as quantum support vector machines and quantum neural networks, could enhance the efficiency of data analysis tasks.
Breakthrough: Faster analysis of large datasets could uncover hidden patterns in biological and chemical data, facilitating the identification of new drug targets and biomarkers.
4. Drug Interaction Prediction:
Current Challenge: Predicting potential drug interactions and understanding their effects on the human body is a complex task.
Quantum Impact: Quantum computers can model the interactions between multiple molecules more accurately, enabling better predictions of drug-drug interactions.
Breakthrough: Enhanced understanding of drug interactions could lead to safer and more effective drug combinations, reducing the risk of adverse effects.
5. Protein Folding and Structure Prediction:
Current Challenge: Predicting the threedimensional structure of proteins accurately is computationally demanding, and classical methods face limitations.
Quantum Impact: Quantum computers can efficiently explore the vast conformational space of proteins, aiding in more accurate predictions of their structures.
Breakthrough: Improved understanding of protein structures could facilitate the design of drugs that target specific proteins with higher precision, leading to more effective therapies.
6. Materials Discovery for Drug Delivery:
Current Challenge: Discovering and optimizing materials for drug delivery systems involves extensive trial and error.
Quantum Impact: Quantum computing can accelerate the discovery of new materials with specific properties, such as controlled drug release.
Breakthrough: Faster identification of optimal drug delivery materials could enhance the efficiency and effectiveness of drug administration.
7. Cryptography for Secure Data Sharing:
Current Challenge: The pharmaceutical industry requires secure sharing of sensitive data for collaborative research.
Quantum Impact: Quantum cryptography can provide secure communication channels, protecting sensitive information from quantum attacks.
Breakthrough: Enhanced security measures could foster greater collaboration and data sharing among pharmaceutical companies and research institutions.
Challenges and Considerations:
While the potential breakthroughs are promising, it’s crucial to acknowledge the current challenges and uncertainties in developing practical and scalable quantum computers. Factors such as error rates, decoherence, and the need for error correction pose significant hurdles.
Additionally, the integration of quantum computing into existing pharmaceutical R&D workflows requires careful consideration of hybrid approaches and compatibility with classical systems.
In conclusion, quantum computing holds great promise for reshaping the computational landscape of pharmaceutical R&D, potentially unlocking new avenues for drug discovery and optimization. However, the realization of these breakthroughs will depend on the continued progress in quantum hardware, algorithm development, and the successful integration of quantum technologies into the pharmaceutical research pipeline.
Question 10. The microbiome’s influence on health and disease is increasingly recognized. How is microbiome research influencing drug development strategies, and what role might it play in the future of personalized medicine?
LAKSHMI RAGHAVAN: There is a big push towards a paradigm shift in healthcare towards personalized and precision medicine through better understanding of the interindividual differences that determines how drugs are tailored to individuals based on genetic and environmental factors. The microbiome, which are trillions of bacteria that live in our bodies, are found to play an important role in drug development, affecting both the
safety and efficacy of the drugs.
Microbiome impacts the efficacy of the drugs through their interactions affecting how the drugs are absorbed, metabolized, and excreted. For example, some bacteria can break down drugs before they have a chance to reach their target, while other bacteria can produce enzymes that activate drugs. The microbiome can also affect the safety of drugs by causing side effects. For example, some bacteria can produce toxins that can harm the body, while other bacteria can interact with drugs to produce byproducts that are a significant risk to the patient.
Researchers are developing innovative therapies that target the microbiome to treat a variety of diseases. A particular example of such therapy is Fecal microbiota transplantation (FMT): FMT is a procedure in which stool from a healthy donor is transplanted into the colon of a patient with a disease. FMT has been shown to be effective in treating recurrent Clostridium difficile (C. diff) infection, a serious type of diarrhea. Other examples include prebiotics and probiotics that are non-digestible fibers and live bacteria respectively. While prebiotics are shown to improve gut health and may be beneficial to prevent and treat diseases such as irritable bowel syndrome and irritable bowel disease, probiotics help to treat diseases such as diarrhea, baginal infections and allergies.
The microbiome also plays a significant role in the development of personalized medicine. Personalized medicine is an approach where the individual’s genetic, environmental, and lifestyle factors are utilized to tailor treatment plans. Such personalized medicine strategies are being adopted for a variety of diseases, such as cancer, autoimmune diseases, and mental health disorders.
The microbiome is a complex and dynamic ecosystem that vary from individual to individual and the mechanism of how and why it differs in different individuals is yet to be understood. However, as newer therapies are innovated and researchers are making progress in the understanding of microbiome’s role in health and disease, the future holds a promise to target personal medicine.
Some potential benefits of using the microbiome to develop personalized medicine over traditional treatment strategies are, improved efficacy, reduced side effects and new treatment options for diseases that are currently difficult to treat, for example, cancer and autoimmune diseases.
The use of the microbiome to develop personalized medicine strategies is still in its infancy. Challenges still remain in the understanding of the interaction of microbiome with drugs that are already approved and in the market. However, future is bright for use of microbiome to radically transform healthcare and how patients are individually treated based on their specific genetic and microbiome
Question 11. Continuous manufacturing is being explored as an alternative to traditional batch processing. How can the pharmaceutical industry leverage continuous manufacturing to enhance efficiency in R&D, and what impact does it have on the scalability of production processes?
SOMESH SHARMA: For an extended period, the pharmaceutical business has restricted its innovation to the exploration and creation of novel active compounds. Meanwhile, the manufacturing structure, which is mostly reliant on batchwise technology, has remained unchanged. In the recent years, the main regulatory agencies (FDA) has recognized the need for a change in drug production and started to promote continuous manufacturing
technologies, and encourage pharmaceutical companies to develop and adapt such processes. Process intensification or continuous manufacturing has many advantages over batch processes, for instance, speed, safety, waste generation, cost effectiveness and building a sustainable process. Continuous manufacturing is also an initiative towards green chemistry with lower consumption of raw materials and generates less waste material. It has made significant progress in pharmaceutical manufacturing from raw materials to the final dosage forms, though, true integration still demands lot of investigation and optimisation. Continuous manufacturing provides flexibility of manufacturing of products in shorter time with high control and quality as transition from laboratory to commercial scale is quite easy. Further, establishment of facilities requires less space in comparison to batch manufacturing plants with reduced overhead costs and helps to meet the global demand of medicine with less disruptions. In summary, the pharmaceutical industry can leverage continuous manufacturing to enhance efficiency in R&D and improve scalability of production processes with precise control over the manufacturing process, reduce the amount of space and raw materials requirements.
MICHAEL N. LIEBMAN: It would seem that continuous manufacturing affords benefits in economy of scale and efficiency and provide potentially greater control of quality. I would expect, however, that this may further limit aspects of flexibility or adaptability and these are key elements in research and development so I see continuous manufacturing more as an operational benefit than a research or strategic benefit.
Question 12. Given the global nature of health challenges, how can the pharmaceutical industry enhance international collaboration in R&D, and what role do multinational partnerships play in addressing global health threats?
SOMESH SHARMA: Covid 19 pandemic has accelerated international collaboration in R&D and build an early warning system for disease outbreak to address global health threats with continuous and transparent partnership to develop new treatments and vaccines. There are various factors driving collaborative environment – i) government funding and partnership with private sector, ii) enhanced international collaboration to address cross border health risks, iii) emerging technologies and conducive environment for data sharing, and iv) increased academia-industrial
One example of a multinational partnership is the Coalition for Epidemic Preparedness Innovations (CEPI), which is a global partnership that aims to accelerate the development of vaccines against emerging infectious diseases. Another example of a multinational partnership is the Global Health Innovative Technology Fund (GHIT), which is a public-private partnership that aims to promote the development of new drugs, vaccines, and diagnostics for neglected diseases in developing countries.
Global partnerships can play a crucial role in addressing global health needs by sharing knowledge, resources, technologies to develop new treatments and vaccines. The collaborations at government level, industryacademia and industrial partnerships to meet unmet demands is crucial for a health society.
Question 13. Neuropharmaceuticals pose unique challenges in drug development. How are advancements in neuroscience and neuropharmacology influencing R&D strategies, and what breakthroughs can we expect in the treatment of neurological disorders?
LAKSHMI RAGHAVAN: The bloodbrain barrier (BBB) serves as a highly selective barrier separating the central nervous system from the systemic circulation. The main challenge with developing treatments for neurological disorders is that the blood -brain barrier restricts the ability of drugs to reach their site of action. Development of delivery strategies for neurological disorders is primarily dependent on the advances made in the understanding of the complexity of the barrier. Some of these advancements are listed below.
As our understanding of the complexities of the brain has advanced, more targeted and effective treatments for neurological disorders are being examined. For example, we now know that many neurological disorders are caused by problems with specific neurotransmitters, such as dopamine or serotonin. This knowledge has led to the development of drugs that can target these neurotransmitters and improve symptoms.
Some of the newer technologies, such as optogenetics and deep brain stimulation, are couple of new techniques that are used to treat neurological disorders. Optogenetics is a technique that uses light to activate or deactivate neurons. Deep brain stimulation is a technique that uses electrical impulses to stimulate or inhibit brain activity. These particularly useful to treat a wide range of neurological disorders, including Parkinson’s disease, epilepsy, and depression.
Biomarkers are biological molecules that can be used to measure the severity of a disease or the response to treatment. Biomarkers are becoming increasingly important in the development of new drugs for neurological disorders. By using biomarkers, researchers can identify patients who are most likely to benefit from a particular drug and track the progress of the disease over time.
Still challenges remain in the advancements in treating neurological disorders and the more we understand the neurological barriers, the better will be the outcome of some of the novel treatments that are being developed. Some of the advanced treatments that we can expect in the treatment of neurological disorders is gene therapy, stem cell therapy and nanoparticle technology that delivers drugs directly to the brain. Moreover, developing a greater understanding of the cellular and molecular mechanisms which control the BBB will enable scientists to optimize the delivery of small molecules, biological therapeutics and diagnostic agents to target sites within the CNS.
Michael N. Liebman: I believe that diseases/conditions in neurological disorders are an example of the real world complexity in disease that is not being adequately addressed at the clinical level. This not a criticism of clinical medicine but more a reflection on the gap between clinical practice and clinical research. I consider the major challenge is the ability to accurately, and reproducibly, diagnose complex diseases with respect to critical stratification and the need to consider disease as a process, not a state, and how this affects the accuracy of a diagnosis. Of course this affects patient treatment and management but it also impacts the ability to effectively identify clinical targets for drug development because of the inherent non-homogeneity of patients diagnosed with a specific condition. In neurological disorders this reflects not only the spectrum nature of many current diagnostic classifications but also the challenge in considering the impact of comorbid conditions both on diagnosisand potential response to therapy.
Question 14. Predictive analytics is becoming more sophisticated in predicting drug safety and efficacy. How can the industry leverage predictive analytics to mitigate risks and optimize decision-making throughout the drug development process?
SHAMAL FERNANDO: Leveraging predictive analytics in the drug development process can significantly enhance decision-making and mitigate risks. Here’s a systematic approach that the pharmaceutical industry can follow to optimize decision-making using sophisticated predictive analytics:
1. Data Integration and Quality Assurance:
Objective: Ensure access to diverse and high-quality data.
Application: Integrate data from various sources, including preclinical studies, clinical trials, real-world evidence, and -omics data. Implement robust data quality assurance processes to enhance the reliability of predictive models.
2. Early Target Identification and Validation:
Objective: Improve the selection of drug targets with a higher probability of success.
Application: Utilize predictive analytics to analyze genetic, omics, and pathway data to identify potential drug targets. Validate and prioritize targets based on predictive models to increase the likelihood of success in later stages.
3. Compound Screening and Optimization:
Objective: Prioritize and optimize lead compounds.
Application: Employ predictive models to assess the pharmacokinetics, toxicity, and efficacy of lead compounds. Identify and optimize promising candidates while reducing the likelihood of late-stage failures.
4. Clinical Trial Design and Optimization:
Objective: Design and optimize clinical trials for efficiency and success.
Application: Utilize predictive analytics to model patient populations, predict enrollment rates, and optimize trial parameters. Implement adaptive trial designs based on real-time data analysis to address emerging insights during the trial.
5. Patient Recruitment and Retention:
Objective: Enhance patient recruitment and retention in clinical trials.
Application: Implement predictive models to identify suitable patient populations and predict recruitment rates. Tailor recruitment strategies based on predictive analytics to improve enrollment and retention.
6. Biomarker Identification and Validation:
Objective: Identify and validate biomarkers for patient stratification.
Application: Use predictive analytics on omics data to identify potential biomarkers associated with drug response. Prioritize biomarkers based on their predictive power and validate their relevance for patient stratification.
7. Adverse Event Prediction and Risk Mitigation:
Objective: Early detection and prediction of adverse events.
Application: Implement predictive models to analyze preclinical and clinical data for potential safety concerns. Proactively address safety issues and optimize risk mitigation strategies.
8. Real-world Evidence (RWE) Analysis:
Objective: Enhance understanding of drug safety and effectiveness in real-world settings.
Application: Analyze real-world data using predictive analytics to assess long-term safety and efficacy. Identify potential safety signals and optimize post-market surveillance strategies.
9. Market Access and Commercialization Strategies:
Objective: Optimize market access and commercial success.
Application: Use predictive analytics to model market dynamics, forecast drug demand, and assess pricing and reimbursement strategies. Anticipate
potential market challenges and adjust commercialization plans accordingly.
10. Continuous Monitoring and Learning:
Objective: Iteratively improve decisionmaking based on ongoing insights.
Application: Establish a continuous learning loop. Regularly update predictive models based on new data, outcomes, and emerging trends to refine decision-making processes.
Considerations for Effective Implementation:
Foster collaboration between data scientists, biostatisticians, clinicians, and domain experts to ensure the development and application of relevant predictive models.
Align predictive analytics practices with regulatory requirements. Demonstrate the validity and reliability of predictive models for regulatory acceptance.
Ethical Data Use:
Implement ethical data practices to protect patient privacy and comply with data governance regulations.
Ensure that predictive models are interpretable, allowing stakeholders to understand and trust the results.
Validation and Calibration:
Regularly validate and calibrate predictive models using new data to maintain their accuracy and relevance.
By following these steps and considerations, the pharmaceutical industry can harness the power of predictive analytics to make informed decisions, reduce risks, and enhance the overall efficiency of the drug development process.
Question 15. 3D printing has applications in drug formulation and personalized medicine. How might 3D printing technologies revolutionize drug delivery systems, and what implications does this have for patient-specific treatment regimens?
LAKSHMI RAGHAVAN: 3D printing has been in existence from the 1980s and since then has increasingly found applications in drug delivery and pharmaceutical development. Three dimensional printing is a layer-by-layer, automated process, which enables the manufacturing of complex, personalized products on-demand. 3D printing applications are several fold and some examples are given below.
Personalized drug dosage and release:
3D printing allows for the fabrication of customized drug formulations with precise dosage and release profiles tailored to individual patient needs. This can be particularly beneficial for patients with complex medical conditions or those who requiremultiple medications.
Improved drug efficacy and reduced side effects:
By controlling the distribution and release of drugs within the body, 3D printing can enhance drug efficacy while minimizing side effects. This is particularly important for drugs with narrow therapeutic windows, where the difference between effective and toxic doses is small.
Development of novel drug delivery systems:
3D printing enables the creation of intricate and innovative drug delivery systems, such as implantable devices, transdermal patches, and inhalers. Specific example is in Microneedles, which have huge applications if the 3D print design appropriately adopted. These systems can provide sustained and controlled drug release, improving patient compliance and reducing the frequency of administrations.
Fabrication of complex drug formulations:
3D printing can combine multiple drugs and materials within a single dosage form, creating complex formulations that would be difficult or impossible to produce using traditional methods. This can lead to the development of more effective combination therapies and personalized treatment regimens.
Implications for patient-specific treatment regimens:
3D printing holds tremendous potential for personalized medicine, enabling the creation of patient-specific drug formulations tailored to individual genetic, physiological, and disease characteristics. This level of personalization can optimize treatment efficacy, minimize side effects, and improve patient outcomes.
Some specific examples of how 3D printing is being used to revolutionize drug delivery systems:
- Personalized oral tablets: 3D printing is being used to create customized oral tablets with precise dosage and release profiles, allowing for individualized treatment regimens. This will benefit a large number of people who would require specific dose regimen based on their physical condition,
- Implantable drug delivery devices: 3D-printed implantable devices can deliver drugs directly to the site of action, providing sustained and controlled release, reducing the frequency of administrations, and improving patient compliance.
- Transdermal drug patches: 3D-printed transdermal patches can deliver drugs through the skin, offering a non-invasive alternative to oral or injectable medications.
- Inhalers for lung diseases: 3D-printed inhalers can deliver drugs to specific areas of the lungs, improving drug efficacy and reducing systemic side effects.
While the success is limited with FDA in the applications of 3Dprinting, the tremendous potential that this technology offers allows the opportunity to explore optimize this technology for commercial applications. As this technology continues to develop, it is only matter of time for more innovative and effective drug delivery systems to emerge, offering hope and providing relief to a large number of people suffering from diseases that is otherwise difficult to treat.
Question 16. As we conclude our discussion on ‘Innovations in Pharmaceutical R&D: Navigating the Future,’ I’d like to invite each panelist to share their perspective on the most critical factor or innovation that they believe will define the future of pharmaceutical research and development. Additionally, what advice would you give to the next generation of researchers and leaders entering this dynamic and evolving field?
LAKSHMI RAGHAVAN: Conventional drug development is a long, tedious, and expensive process. With digital health exploding in the last few years, understanding and use of artificial intelligence and machine learning to different stages of drug development will be critical to the development of new drugs in a much shorter time and at the same highly precise. Starting from drug discovery, artificial intelligence will play a big role in product
development, clinical trials to manufacturing, real world evidence and commercial settings. The key challenges that still exist are the trust or the lack of it in the AI models and the inertia for a change from within the pharmaceutical companies and regulatory agencies. It is heartening to see that perspective changing with lots of initiatives in the pharmaceutical industry as they start to collaborate with technology companies to come up with innovative solutions. The future hold a lot of promise for the next generation to continue exploring digital applications in not only development but also they should focus on precision and personalized medicine, which is critical to a large number of patients suffering from diseases that would require specific dosing regimen for a successful outcomes.
SOMESH SHARMA: There are several elements that could influence the pharmaceutical industry’s future as it is constantly evolving. Everyone primary goal is to benefit society, which calls for creative solutions, knowledge and data sharing, storage and analysis, developing ML and AI models which are more friendly and predictive, enhanced industry to-industry cooperation, government sponsorship, and academia-industry partnerships. The future of treatment is going to change with more digital therapies, precision medicine and personalised therapies instead of universal treatments.
To reduce its influence on environment, the pharmaceutical business is also embracing green initiatives and sustainability. This includes lowering carbon emissions by using renewable energy sources, energy-efficient production, continuous manufacturing, minimising waste, utilising eco-friendly packaging materials, creating more environmentally friendly chemical synthesis techniques (green chemistry), and consuming less water throughout the manufacturing process.
For the next generation, a transformational mindset for steadfast collaborative endeavors will be essential, given the complexity of today’s healthcare concerns and the speed at which technology is developing.
Critical Factor: Integration of Artificial Intelligence.
Advice: Embrace interdisciplinary collaboration, stay agile in technology adoption, and prioritize ethical considerations.
Critical Factor: Advancements in Personalized Medicine.
Advice: Master genomic technologies, prioritize patient-centric approaches, and adapt to evolving technologies.
Critical Factor: Real-world Evidence and Data Analytics.
Advice: Develop strong data analytics skills, advocate for robust RWE integration, and champion transparent communication.
Critical Factor: Quantum Computing in Drug Discovery.
Advice: Understand quantum principles, collaborate with experts, and stay adaptable in the evolving quantum landscape.
Critical Factor: Predictive Analytics for Risk Mitigation.
Advice: Hone predictive modeling skills, prioritize data quality, and foster a mindset for continuous learning in drug development.
Michael N. Liebman: I believe that the most critical factor for improving the development of effective drugs and reducing the current rate of failure in drug development will require greater emphasis on accepting the complexities of the real world. Efforts to address patient complexity with a focus on diversity is a positive step but limited as it focuses on age, gender, ethnicity, disability, etc but does not address the real world issues of co morbidities, poly-pharmacy, etc.
Complexity in defining disease and diagnosis needs to address the complexity that disease is a process and not a state and the temporal development of disease needs to be considered to enhance diagnosis and stratification. This impacts target identification, drug development and clinical trial design as well. And it is also critical, early in drug development, to understand the factors impacting clinical practice, e.g. guidelines, biases, experience, reimbursement issues, training. At the end of the day in pharma R&D it is not sufficient to only have a drug that works in clinical trials and can be approved by regulatory agencies for use, it is critical to have a drug that physicians are willing to prescribe and patients are willing to take.