In the intricate web of human health, the immune system stands as a vigilant guardian, tirelessly defending against potential threats. At the forefront of understanding, deciphering, and harnessing the immune system’s power is the immunologist. This article delves into the captivating realm of immunology, shedding light on the indispensable role played by these medical experts in maintaining our well-being.

Immunology

Immunology is the scientific study of the immune system – a complex network of cells, tissues, and molecules working harmoniously to safeguard the body against invading pathogens, such as bacteria, viruses, and other harmful entities. At the heart of this field are immunologists, experts dedicated to unraveling the mysteries of immune responses and applying this knowledge to enhance human health.

Becoming an Immunologist: The Journey and Expertise

Becoming an immunologist requires a rigorous educational path. Typically, individuals pursuing this profession acquire a bachelor’s degree in a related field like biology, followed by advanced degrees such as a Master’s or Ph.D. in Immunology or a related discipline. This academic journey equips them with a profound understanding of the immune system’s complexities.

Decoding the Immune System

Components of the Immune System

The immune system is a symphony of diverse cells and molecules, each with a specific role to play. White blood cells, antibodies, cytokines, and complement proteins collaborate to recognize and neutralize threats.

Innate vs. Adaptive Immunity

Immunologists distinguish between innate and adaptive immunity. Innate immunity provides rapid but generalized defense, while adaptive immunity offers tailored, long-lasting protection based on prior exposures.

The Role of Immunologists

Disease Detection and Diagnosis

Immunologists contribute significantly to diagnosing various diseases by analyzing immune responses. Techniques like ELISA and flow cytometry aid in identifying infections, allergies, and autoimmune disorders.

Research and Development

Immunologists spearhead groundbreaking research, driving medical innovation. They investigate new therapies, study immune-related disorders, and develop advanced diagnostic tools.

Treatment and Management

In collaboration with other medical professionals, immunologists design targeted treatments and therapies. Immunotherapy, such as monoclonal antibody treatments, harnesses the immune system to combat diseases like cancer.

Immunology in Action: Real-Life Applications

Vaccination and Immunization

Immunologists have played a pivotal role in developing vaccines that prevent deadly diseases like polio, measles, and COVID-19. These efforts have saved countless lives worldwide.

Autoimmune Disease Management

Understanding the intricacies of immune responses has led to improved management of autoimmune diseases, offering relief and enhanced quality of life for those affected.

Cancer Immunotherapy

Cutting-edge cancer treatments involve immunotherapy, where the immune system is activated to recognize and eliminate cancer cells. This promising approach offers new hope in the fight against cancer.

The Ongoing Pursuit of Knowledge: Advancements in Immunology

Genetic Immunology

Advancements in genetic research have opened avenues to understand how genetics influence immune responses. This knowledge holds the potential to tailor treatments based on individual genetic profiles.

Immunoinformatics

Immunoinformatics merges immunology with bioinformatics, accelerating the discovery of new vaccines and therapeutic targets by analyzing vast amounts of immunological data.

Collaboration in the Medical Landscape: Interdisciplinary Approach

Immunology and Microbiology

Immunologists collaborate with microbiologists to decipher the intricate relationship between microorganisms and the immune system. This partnership enhances our understanding of infections.

Immunology and Oncology

In oncology, immunologists collaborate to develop personalized treatments that boost the immune response against cancer cells, leading to improved outcomes for patients.

Challenges and Ethical Considerations

Emerging Infectious Diseases

Immunologists are at the forefront during outbreaks of new infectious diseases, where rapid response and understanding of immune reactions are crucial for containment.

Ethical Implications of Immunotherapy

While immunotherapy holds immense promise, ethical considerations arise due to its potential side effects and the fine balance between enhancing immune responses and avoiding harm.

The Path Ahead: Immunologist’s Role in Public Health

Immunologists play a pivotal role in shaping public health policies and strategies, especially in the context of vaccination campaigns, disease outbreaks, and global health crises.

Conclusion

In the realm of health and medicine, immunologists stand as sentinels of the immune system, deciphering its complexities and harnessing its power to conquer diseases. Their expertise drives medical advancements, offering hope for healthier lives.

FAQs

What is the main focus of immunology? Immunology focuses on studying the immune system’s functions and responses to maintain health and combat diseases.

How do immunologists contribute to cancer treatment? Immunologists contribute by developing therapies that enhance the body’s immune response against cancer cells.

What are some examples of autoimmune diseases? Examples include rheumatoid arthritis, lupus, and multiple sclerosis, where the immune system attacks the body’s own tissues.

Can immunotherapy replace traditional cancer treatments? Immunotherapy is a valuable addition to cancer treatments, but its role varies based on the type and stage of cancer.

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The field of clinical trials and pharmacovigilance (PV) is constantly evolving, driven by advances in medical science, technology, and regulatory requirements. As we look ahead to the next five years, it is evident that significant changes and innovations will shape the landscape of these crucial areas. In this article, we will explore the upcoming advancements in clinical trials and PV that are set to revolutionize the healthcare industry.

Leveraging Artificial Intelligence (AI) and Machine Learning in Clinical Trials

In the coming years, AI and machine learning will play a pivotal role in transforming the way clinical trials are conducted. These technologies have the potential to streamline various processes, enhance data analysis, and improve patient recruitment. AI-powered algorithms can quickly identify suitable candidates for specific trials based on their medical history, genetic makeup, and other relevant factors. This efficiency will lead to reduced costs and faster trial completion, ultimately benefiting patients and researchers alike.

Decentralized Clinical Trials (DCTs) and Virtual Trials

Traditional clinical trials often involve the physical presence of participants at specific research sites. However, DCTs and virtual trials are gaining momentum, especially after the COVID-19 pandemic accelerated the adoption of remote healthcare solutions. With DCTs, patients can participate from the comfort of their homes, eliminating the need for frequent travel and reducing logistical complexities. This approach increases patient diversity and expands the reach of clinical trials, providing a more representative sample for research purposes.

Personalized Medicine and Targeted Therapies

The era of personalized medicine is dawning upon us. Advancements in genomics and molecular diagnostics have opened doors to targeted therapies tailored to an individual’s specific genetic makeup and disease characteristics. This approach can lead to more effective treatments with fewer side effects, as medications are optimized for each patient’s unique biology. As we progress, personalized medicine will become increasingly common in clinical trials and drug development, revolutionizing patient care.

Real-World Evidence (RWE) and Post-Marketing Surveillance

While clinical trials provide valuable data, real-world evidence (RWE) is gaining importance in assessing a drug’s performance in the broader population. RWE encompasses data from electronic health records, claims databases, and patient registries, offering insights into long-term safety and effectiveness. Regulatory agencies are increasingly relying on RWE to support post-marketing surveillance, which will influence drug approvals and label updates in the future.

Blockchain Technology for Data Security and Integrity

The decentralized and immutable nature of blockchain technology makes it an ideal candidate for enhancing data security and integrity in clinical trials. By using blockchain, researchers can ensure that sensitive patient data remains confidential and unaltered, reducing the risk of data breaches and fraud. Additionally, smart contracts on the blockchain can automate various trial processes, making them more efficient and transparent.

Wearable Devices and Digital Endpoints

Wearable devices and digital endpoints are revolutionizing how clinical trials collect data. These devices can continuously monitor patients’ vital signs, activity levels, and other health-related metrics in real-time. By providing a constant stream of data, wearable devices enable researchers to have a deeper understanding of a drug’s effects on patients over an extended period. This data-rich environment will lead to more informed decision-making and better treatment outcomes.

Regulatory Evolution and Streamlined Approvals

Regulatory agencies worldwide are recognizing the need to streamline drug approvals and reduce time-to-market for critical medications. Consequently, they are adopting innovative approaches to expedite the approval process while ensuring safety and efficacy. Such changes will encourage pharmaceutical companies to invest in research and development confidently, knowing that they can bring life-saving drugs to patients more efficiently.

Patient-Centricity and Inclusive Trial Design

Patient-centricity will continue to be a driving force in shaping the future of clinical trials. Researchers are placing a greater emphasis on involving patients in trial design and decision-making processes. By understanding patients’ needs and preferences, researchers can design trials that are more inclusive and better aligned with real-world scenarios. This approach leads to higher patient engagement, improved retention, and ultimately, more reliable trial results.

Advanced Data Analytics and Predictive Modeling

Data analytics will become even more sophisticated, allowing researchers to derive valuable insights from vast amounts of data generated during clinical trials. Predictive modeling can help identify potential safety issues early in the development process, preventing adverse events and facilitating better risk management. Furthermore, advanced analytics can aid in patient recruitment, ensuring that trials enroll the right candidates efficiently.

Global Collaboration and Data Sharing

Collaboration among researchers, pharmaceutical companies, and regulatory authorities across borders is vital to address global health challenges effectively. By sharing data and knowledge, stakeholders can collectively work towards developing innovative treatments for rare diseases and unmet medical needs. Initiatives like data-sharing platforms and international research consortia will foster a more interconnected and collaborative research ecosystem.

In conclusion, the future of clinical trials and pharmacovigilance is bright and promising. The integration of cutting-edge technologies, patient-centric approaches, and streamlined regulatory processes will propel medical research to new heights. Embracing these future trends will not only accelerate drug development but also improve patient outcomes and contribute to advancing healthcare on a global scale.

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In the fast-paced world of supply chain management, warehouse efficiency plays a pivotal role in ensuring timely and accurate order fulfillment while minimizing costs. With the rising demands of e-commerce and globalization, businesses are continually seeking innovative ways to optimize their warehouse operations. This article delves into the significance of warehouse efficiency, the challenges faced, the role of technology, and best practices to achieve maximum productivity.

Understanding Warehouse Efficiency

Defining Warehouse Efficiency

Warehouse efficiency refers to the ability of a warehouse to utilize resources effectively and deliver products to customers efficiently. It involves streamlining processes, reducing wastage, and maintaining accuracy in inventory management.

The Importance of Efficient Warehousing

Efficient warehousing is crucial for businesses to stay competitive in today’s market. It ensures swift order processing, minimizes holding costs, and enables faster response to customer demands.

Identifying Challenges in Warehouse Operations

Inventory Management

Maintaining optimal inventory levels while avoiding stockouts and overstock situations can be a complex challenge for warehouse managers.

Order Picking and Packing

Inefficient order picking and packing processes can lead to delays and errors in fulfilling customer orders.

Space Utilization

Limited warehouse space and inefficient use of available space can restrict the storage capacity and lead to operational bottlenecks.

The Role of Technology in Improving Warehouse Efficiency

Warehouse Management Systems (WMS)

A WMS provides real-time visibility into inventory levels, automates order processing, and optimizes warehouse layouts for enhanced efficiency.

Automation and Robotics

Automated systems and robotics streamline repetitive tasks, such as picking, packing, and loading, resulting in increased productivity and reduced human errors.

IoT (Internet of Things) in Warehousing

IoT sensors enable real-time tracking of inventory, monitor equipment health, and offer predictive maintenance insights.

Data Analytics and Artificial Intelligence (AI)

Data-driven insights and AI-powered algorithms help warehouse managers make informed decisions, predict demand patterns, and optimize supply chain operations.

Implementing Best Practices for Enhanced Efficiency

Efficient Layout and Design

An organized and logical warehouse layout improves material flow and reduces the time taken to process orders.

Optimized Inventory Management

Regular audits, ABC analysis, and adopting just-in-time (JIT) inventory practices lead to efficient stock management.

Streamlined Order Fulfillment Process

Integrating order processing, inventory management, and shipping systems streamline the entire fulfillment process.

Employee Training and Engagement

Well-trained and motivated warehouse staff contribute significantly to improved efficiency and reduced errors.

The Benefits of Improving Warehouse Efficiency

Cost Savings

Enhanced efficiency leads to reduced operational costs, better resource utilization, and minimized waste.

Increased Productivity

Automation and streamlined processes result in higher productivity and quicker order processing.

Customer Satisfaction and Loyalty

Efficient warehousing ensures timely deliveries, leading to increased customer satisfaction and loyalty.

Overcoming Challenges and Resistance to Change

Addressing Workforce Concerns

Incorporating technology requires addressing employee concerns and providing training to adapt to new systems.

Emphasizing Long-Term Gains

Promoting the long-term benefits of efficiency improvements helps gain support for technology adoption.

Case Studies: Successful Implementation of Technology and Best Practices

Company A: Automating Order Processing

Company A achieved significant efficiency gains by implementing an automated order processing system, reducing order fulfillment time by 40%.

Company B: IoT Integration for Real-Time Inventory Tracking

By integrating IoT sensors for real-time inventory tracking, Company B improved inventory accuracy and reduced stockouts by 30%.

Company C: Data-Driven Predictive Analytics

Company C leveraged data-driven predictive analytics to optimize inventory levels, resulting in a 25% reduction in carrying costs.

Future Trends in Warehouse Efficiency

Advancements in Automation and Robotics

Advancements in automation and robotics will lead to even greater efficiency gains and reduced reliance on manual labor.

AI-Driven Warehousing

AI will revolutionize warehousing with intelligent decision-making, predictive analysis, and efficient resource allocation.

Sustainable and Green Warehouses

The future will witness the rise of environmentally conscious and sustainable warehouses, focusing on eco-friendly practices.

Conclusion

Improving warehouse efficiency is a critical component of modern supply chain management. By leveraging technology and adopting best practices, businesses can achieve higher productivity, cost savings, and customer satisfaction. Embracing these changes and staying ahead of emerging trends will enable warehouses to thrive in the dynamic business landscape.

FAQs

1. What is the significance of warehouse efficiency? Warehouse efficiency ensures timely order fulfillment and minimizes costs, contributing to overall business success.
2. How does technology enhance warehouse efficiency? Technology such as WMS, automation, IoT, and AI streamlines processes, optimizes inventory management, and improves overall productivity.
3. What are some best practices for efficient warehousing? Efficient layout and design, optimized inventory management, streamlined order fulfillment, and employee training are key best practices.
4. How can warehouse managers overcome resistance to change? Addressing workforce concerns and emphasizing the long-term benefits of efficiency improvements can help overcome resistance to change.
5. What are the future trends in warehouse efficiency? Advancements in automation, AI-driven warehousing, and sustainable practices are the future trends to watch for in warehousing.

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Malaria represents one of the greatest unmet clinical needs in the world. In the last decade, research has been ongoing to develop effective preventative methods and effective treatment to reduce the risk of severe illness, which typically occurs in children. With only one malaria vaccine on the market, the industry is beginning to shift its approach towards the mRNA platform as a potential vaccine, which could be safer and more effective in the long-term. 

Introduction 

Malaria is a life threatening disease caused by the transmission of a parasite via infected female mosquitos. Despite the disease being preventable and curable, there were an estimated 229 million cases of malaria worldwide in 2019. According to WHO, the WHO African Region carries a disproportionately high proportion and accounts for 94% of the global malaria cases and deaths in 2019. 

In the majority of cases, malaria is transmitted through female Anopheles mosquitoes who typically targets prey between dusk and dawn. These mosquitoes are known as vector species as they are the vehicle through which the parasite is transmitted. While there are approximately 400 different species of Anopheles Mosquitoes, around 30 are malaria vectors of significant importance. 

Significant progress has been made in the prevention of malaria over the last decade, with the total funding for malaria control and elimination reaching an estimated US $3 billion in 2019. Prevention techniques have been the main way to prevent and reduce malaria transmission and primarily comprise insecticide-treated mosquito nets and antimalarial drugs.

Insecticide-treated mosquito nets have proven to significantly reduce the risk of malaria transmission, especially so in the African population. According to WHO, in 2019, an estimated 46% of all people at risk of malaria in Africa were protected by an insecticide-treated net, compared to 2% in 2000.

Despite these interventions, malaria continues to pose a significant risk across the globe and represents an unmet clinical problem with a relatively high number of cases. The rise of antimalaria drug resistance is a recurring problem that continues to emerge in high risk regions, posing a problem for the prevention of the disease which can be fatal in young children. 

The development of a vaccine for malaria has been a key focus for the last five years, in order to significantly reduce the transmission of malaria and life-threatening complications. While the insecticide nets provide some protection against the mosquitos, these nets are not always available to access and also do not protect against severe disease. Furthermore, compared to antimalarial drugs, a malaria vaccine would offer more long-term protection.

Current progress

RTS,S/AS01 vaccine

To date, the only malaria vaccine on the market is GSK’s Mosquirix, also known as RTS,S/AS01 vaccine. The RTS,S/AS01 vaccine acts against the most deadly and prevalent malaria parasite in Africa – P. falciparum. 

In comparison with other vaccines, the RTS,S demonstrates a higher level of immunity with a booster dose than the primary vaccination. The studies are ongoing for this vaccine but show significant promise. A recent clinical trial by Oxford University’s Jenner Institute showed that its experimental RTS,S/AS01 malaria shot “could be around 77% effective against the disease”.

Currently, the RTS,S/AS01E malaria vaccine is the most advanced so far, having demonstrated promising results in the latest phase 3 clinical trials – specifically, a significant reduction in malaria (and life-threatening malaria) in young african children. The vaccine was tested in a phase 3 clinical trial of a three-dose immunisation schedule, with a fourth dose 18 months after the primary vaccination. According to a recent publication, the booster dose partly restored the waning vaccine efficacy which has been seen in infants rather than children.   

In a recent publication, it has been highlighted that WHO has recognised the potential of the RTS,S/AS01 vaccine for public health and “acknowledged the need for further evaluation before individual countries consider adopting its use in routine vaccination schedules”. 

Whole Sporozoite Vaccines (WSV)

Another promising malaria vaccine candidate include the whole sporozoites – sporozoites are a defined as a “a motile spore-like stage in the life cycle of some parasitic sporozoans (e.g. the malaria organism), that is typically the infective agent introduced into a host”.

Experimental malaria vaccines using whole sporozoites have shown to produce an increased range of immunogens in comparison with subunit vaccines across at least two life cycle stages of the parasite. Immunogens simply refers to a specific type of antigen which elicits an immune response. 

One of the desired properties of these vaccines has been that WSVs can elicit immunity without causing the clinical symptoms of malaria. However, there remain a number of challenges for this vaccine including the manufacturing of large quantities of sporozoites for vaccine commercialisation. In addition, a recent WSV failed to provide sufficient protection in pre-exposed individuals. 

Therefore, it has been suggested that improved dosing strategies or perhaps an alternative vaccine approach is needed for populations in malarious regions. 

BioNTech mRNA vaccines

Despite the success of RTS,S vaccines so far, a number of limitations including sterile efficacy and duration of protection, have seen the industry look for alternative approaches for malarial vaccination.  

The recent success with mRNA-based COVID-19 vaccines has highlighted the advantages of mRNA-based platforms for vaccinations. The greater specificity of targeting, scalable manufacturing and eliciting of a strong immune response has opened the door for utilising these for malarial vaccines. 

BioNTech aims to develop the world’s first mRNA-based vaccine by the end of 2022, following the launch of it’s ‘Malaria Project’.  The development of a safe, effective mRNA vaccine is one of two objectives for the malaria project. While this vaccine will still rely on P. falciparum’s circumsporozoite protein to generate an immune response, this approach will use mRNA to synthesise the protein to trigger an immune response (like the COVID-19 mRNA vaccine). The vaccine will also utilise a lipid nanoparticle as a vector for the mRNA material. 

The second objective of the malaria project is the development of “sustainable vaccine production and supply solutions on the African continent”. 

A recent preclinical study has already demonstrated sterilising immunity against malaria. Evaluation of PfCSP (circumsporozoite protein) mRNA as a malaria vaccine found that the candidate was found to be well expressed in a number of preclinical models including mammalian cells, immunogenic in mice, and protective in both homologous and heterologous transgenic rodent models.

A quote from the lead author in a recent article supported the potential of mRNA-based malarial vaccines, highlighting that “while more work remains before clinical testing, these results are an encouraging sign that an effective, mRNA-based malaria vaccine is achievable.”

The success of the mRNA-based COVID-19 vaccine and the significant investment in RNA research supports the viability of developing a safe and effective vaccine for malaria with long-term protection for those most at risk. The advancement of an mRNA-based malarial vaccine is dependent on further preclinical studies and hopefully, future human clinical trials.

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

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Intrinsically disordered proteins (IDPs) are under the limelight in cancer research. While research is continuous in understanding their dynamic structure, their implication in tumour development is becoming increasingly evident. Some of the latest advancements demonstrate how IDPs can be used to develop a promising, novel approach to drug design. 

Introduction

IDPs are a group of proteins recently recognised that challenge the paradigm of the protein folded structure. Many biologically active proteins comprise a unique, well-defined structure required for their specific function. IDPs however, do not follow this previously defined rule.

These proteins fail to form unique 3D structures under physiological conditions and are structurally very differently from ordered protein. However, they possess a number of crucial biological functions that complement structured proteins including cell signalling and protein-protein interaction.

Their involvement in important biological pathways, however, means that any dysregulation that arises within proteins is typically associated with disease. Dysregulation can arise with misexpression or mismodification which can cause IDPs to engage in unwanted interactions, inducing the development of pathological conditions. Many of the proteins associated with neurodegeneration, cancer, diabetes and cardiovascular disease are IDPs.

Their association with diseases like Parkinson’s and Alzheimer’s has made them attractive drug targets and further research is being conducted to better understand their dynamics. According to a recent review, a number of IPD-targeting compounds have been developed and inhibitors have reached the clinical trial stage demonstrating their druggability. Unfortunately, none of these compounds have reached the market as commercially available drugs.

When targeting IDPs, there are a number of challenges to overcome. Firstly, their inability to adopt a well-defined 3D structure creates obstacles for developing complementary ligands that may need to bind. Secondly, actually understanding the molecular basis of IDPs has been difficult to determine as the conventional techniques do not have the capacity to do so. 

As techniques and technology evolve however, it is clear to see that IDPs are implicated in certain human disease states. The goal is to better define and understand the dynamic nature and structure clearly enough to create IDPs as potential leads for drug development. 

Cancer pathology

The tumorigenic variations that occur at the genome level manifest as alterations in protein properties such as structure, localisation and stability. Therefore the structural, and consequently functional, properties of the affected proteins determine their implication in cancer development.  

Approximately 70% of human cancer-associated proteins have been predicted to contain relatively long, unstructured regions. In addition, the implication of IDPs in gene regulation and protein networks supports the evidence that says IDPs play a role in cancer pathology. However, it has not been systematically analysed whether the mutations of intrinsically disordered regions/proteins have a direct role driving cancer development or what molecular functions and biological processes are altered by these events.

A recent study used an integrated computational approach which identified a set of cancer drivers specifically targeted by mutations in disordered regions. IDRs represent approximately 30% of the residues found in the human proteome and are also a critical component of many cancer-associated proteins. 

These mutations which drive cancer development have shown to be present across a “wide range of cancer types, and can also be the main, or one of the main, driver events for several tumor subclasses, including both malignant and benign cases”.

However, it has been suggested that the association between protein disorder and cancer is more likely indirect – a recent analysis found that cancer-associated missense mutations had a preference for ordered regions. In other words, the extent to which IDPs and IDRs are implicated in mechanisms driving cancer are still largely unexplored.

Therapeutic advancements 

The identification of functional modules that are directly altered in cancer driver genes, like IDPs, can serve with potential targets for pharmaceutical intervention and have been a focus in recent years.

Currently, drug development is mainly focused on ordered protein domains, however IDPs could serve as a new direction for cancer therapeutics. The more recent approaches have been trying to directly target IDPs via small compounds or blocking the interaction partner of IDPs. The molecular strategies to do this however can be radically different and are not yet widespread across the field. 

A 2017 study reviewed the therapeutic interventions of cancers using intrinsically disordered proteins as drug targets, using c-Myc as a model system. The dysregulation of multiple transcription factors (TFs) have been reported in cancer progression, p53 and c-Myc proteins especially. 

Myc proteins (classified as c-myc, N-myc, and L-myc) are TFs that serve as central regulators of many physiological processes including apoptosis, cell proliferation and biosynthesis of proteins. Studies on animal mouse models have demonstrated that inhibition of c-Myc can “completely stop tumour growth and can also inhibit cancer stem cell progression”.

Latest advancements 

In terms of the latest advancements, exciting research has recently been published detailing the designing of a drug candidate, using IDPs, for bladder cancer. The new approach involves the use of an IDP complex found in human milk known as HAMLET – which, when partially unfolded, has shown incredible cancer-killing abilities. 

According to the leader of the project, Dr Ken H Mok, the clinical trials already show great impact in “reducing tumor size in people with this form of bladder cancer without any side-effects”. This is a hugely exciting step in cancer therapeutics, which is contributing to a novel approach to the conventional lock/key molecular drug design.

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

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RNA has proven in the last decade to be a valid therapeutic target for a multitude of diseases. The ability to manipulate the genetic code to produce almost any protein is one of the many advantages that has also led to the development of RNA-based vaccines. We look at many positive outcomes continued research into this molecule can bring, including successful mRNA COVID-19 vaccines and a new class of drug targets for triple-negative breast cancer. 

Introduction to RNA

RNA is a molecule made up of small building blocks called nucleotides. RNA is primarily single-stranded, however special RNA viruses exist as double-stranded. RNA is synthesised from DNA by an enzyme known as RNA polymerase in a process called transcription. The primary function of RNA is protein synthesis which occurs in a process called translation. Other critical functions include the modification of other RNAs and regulation of gene expression during growth and development. The three main forms of RNA involved in protein synthesis are mRNA (messenger RNA), rRNA, and tRNA.

In terms of their association with disease, genetic mutations that occur in RNA are a problem. Errors in the sequence of nucleotides can result in defects in the RNA complex impacting the cellular processes that RNA maintains. As a result, disruption to cellular processes can lead to disease development. 

A 2018 review highlighted the specific mutations in the gene coding for RNA exosome subunits that cause human disease. The RNA exosome is a multi-unit protein complex that catalyses the processing or degradation of many types of RNA. According to another review of RNA, mutations in the exosome subunit genes EXOSC3 and EXOSC8 have been associated with pontocerebellar hypoplasia, a group of rare conditions characterised by an abnormal prenatal development of the cerebellum and brainstem. The abnormally small cerebellum and brain stem result in defined psychomotor retardation. Manifestations of psychomotor retardation include “slowed speech, decreased movement, and impaired cognitive function”.

Principles for targeting RNA with drug-like small molecules

With diseases like pontocerebellar hypoplasia which support the implication of RNA mutation, it opens the door for targeting RNA with drug-like small molecules. The great diversity in the structure and function of RNAs increases the number of options for druggable targets. 

Several studies establish that RNA is a valid target of small molecules, and that ligands can bind to isolated binding pockets in RNA. One such discovery was “the development of small molecule screens against functionally defined viral RNA motifs, such as human immunodeficiency virus-1 (HIV-1)”.

There are three main components necessary to enable optimal RNA-targeted drug discovery as highlighted in a Nature article: (1) valid therapeutic RNA target, (2) suitable screening approach to identify ‘drug-like lead molecules’ with the desired pharmacological properties, and (3) establishing RNA motifs with high specificity and potency binding to a high quality pocket. It was also emphasised that ensuring the ligand-binding properties of the RNA target was equally as important as the validated target RNA itself.

Why are pharmaceutical companies so eager to exploit RNA for therapeutic purposes?

Previously RNA therapeutic research was conducted in academia by labs who employed scientists with expertise in cellular biology and assay development. In the last few years however, the increasing collaboration between pharma and academia has seen many pharmaceutical companies expand their pipelines in RNA research. 

There are a number of reasons as to why pharma companies and biotechs are drawn to developing RNA therapeutics. Advantages of RNA-based drugs that are driving development include: 

(1) Their ability to act on targets that are otherwise “undruggable” for a small molecule or a protein

(2) Their rapid and cost-effective development, in comparison to small molecules or recombinant proteins

(3) The ability to rapidly alter the sequence of the mRNA construct for personalised treatments or to adapt to an evolving pathogen. 

Continuous advancements in stability, chemical modification and delivery systems will no doubt continue to see increased commercialisation of RNA therapeutics. In the last few years, the industry has seen a number of significant collaborations and partnerships to further RNA research and development. Independent pharma group Servier recently entered a strategic collaboration with Nymirum, described as a “pioneer in RNA-targeted small molecules”. 

According to a recent news article, the collaboration hopes to “identify and develop RNA-modulatory drugs for the treatment of neurological diseases”. Neurodegenerative disorders especially are difficult to treat with small molecules due to complex pathology, and the potential to target undruggable makes RNA-based therapeutics a desirable approach.

Therapeutic applications

RNA-based vaccines

Vaccines take advantage of the highly-evolved human immune system, and essentially trick it into thinking it has become infected in order to promote the production of antibodies by B cells. 

Live attenuated, inactivated pathogens and subunit vaccines are conventional vaccines which have proven successful against many diseases including polio and smallpox. However there have been flaws, with some able to evade the adaptive immune response.

mRNA vaccines are a promising alternative to conventional approaches to vaccination. These vaccines initiate an immune response by introducing mRNA into the cells which have been engineered to carry the genetic instructions to produce the pathogen antigen. Through protein synthesis, the antigens are created which stimulate an immune response in the host and antigen copies are stored by memory cells for future immunity.

The many advantages to using RNA-based vaccines have been acknowledged for a number of years and are highlighted in a 2018 Nature article:

• mRNA-based vaccines are non-infectious

• mRNA is degraded by normal cellular processes, so there is no naked genetic code remaining in the host’s cells

• mRNAs possess an ‘inherent immunogenicity’. Immunogenicity refers to the ability of a foreign substance to induce an immune response. This “can be down-modulated to further increase the safety profile”.

• Production of mRNA vaccines shows potential for rapid, inexpensive, and scalable manufacturing

Potential new class of drug target for breast cancer: RNA-binding protein 

RNA-binding proteins have been under the spotlight over the last couple years as a potential therapeutic target for breast cancer. A study recently published in July this year discovered a number of RNA-binding proteins within human cells and mouse models of cancer, specifically YTHDF2. 

Mice models were developed from the transplant of human cells from triple-negative breast tumours into the rodent. It was found that with the removal of YTHDF2 from the tumours shrank “approximately 10-fold in volume”.

This represents an exciting era for cancer treatment, with the potential to develop precision medicine that is more effective, with fewer side effects and offers hope for patient populations with treatment resistance and rare cancer types.

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

The post RNA Therapeutics: Innovations in Disease Research and Drug Development appeared first on Proventa International.

3D cultures and organ-on-a-chip are a few examples of technologies that hope to bridge the gap from benchside to clinic. Failures that arise during drug development often arise due to poor translatability from models in preclinical studies to the patients in clinical trials. We spoke to Marcie Glicksman, Head of Biology for Enclear Therapies, about her thoughts and experience with novel technologies supporting translational medicine. 

Proventa International: At our most recent event you discussed some of the new tools/technologies addressing the translational medicine gap – could you explain further what translational medicine is and some of the main challenges that arise in drug development?

Marcie Glicksman: People use this term ‘translational medicine’ very broadly, but it’s one of the reasons why I went into the world of drug discovery. Translational medicine is the foundation of science combined with technology for the purpose of better medicine. 

There is room for improvement at every stage in drug development. Starting with the clinical stage, finding a patient population can be a huge issue i.e. trying to find biomarkers that define patient populations. 

Medicine is not based on mechanisms but on the symptoms. Consequently we have a lot of drugs that alleviate symptoms, which is a step in the right direction, but we’re not curing any diseases.

PI: Do you believe we should be approaching drug development from a bottom-up approach to top-down, or is it dependent on the disease?

MG: It can depend on the disease – for example, you have enzyme replacement therapies that can make sense for a bottom-up approach. I like a top-down approach, as it is our purpose to cure or ameliorate diseases so we can understand what is an appropriate approach. As a researcher, having conversations with clinicians at an early stage is good as you are not always aware of the issues that plague patients, and so if you can provide something to address this issue, it can be important for the patient – so that’s why I do like the top-down approach.. 

PI: Do you think emerging technologies will support or replace these animal models at the preclinical stage?

MG: When I started working with stem cells, eight or nine years ago, I was very hopeful that they would replace animal studies. There have been various studies surrounding cardiotoxicity and liver toxicity in which stem cells may eventually provide better models. Also in terms of predicting patient response, if you can have patient cells which you can test and see they respond, then maybe they can be more predictive than animal models. 

Animal models are useful from a target-engagement standpoint, and potentially a dosing standpoint, if you take into account species differences etc… But there are a lot of logistical differences. If you look at rodents for example, their brains are very different and much smaller than humans’. Non-human primates have a complexity to their brain but their brains and body sizes are a lot smaller.

For CNS delivery, we’ve been using sheep. I don’t have the evidence yet, but I think in some ways, sheep could be a better model than non-human primates.They have more cerebrospinal fluid, they have a brain closer to the size of a human’s and body weight which is closer to a human. 

I also think we have got a lot better in terms of predicting toxicity – a lot of drugs fail because they’re not efficacious but they’re completely safe. 

PI: 3D cultures are an example of the latest advancements in cell-based assays – how are 3D cultures created? What advantages do 3D cell cultures have over 2D cell cultures in terms of modelling?

MG: Cells have a natural affinity for each other- our tissues are formed by our cells creating junctions between each other, as they like to adhere. You can create 3D cultures by creating an environment where culture media are moving, whether this be in multi-well plates or in mini-spinner flasks- you can form them pretty easily. 

3D cultures are a lot closer to the physiological state, and so far, a lot of people have been comparing 3D to 2D cultures. One of the roundtables I had at a recent Proventa event involved people who had been using 3D cultures, and sharing how their stem cells had grown and differentiated better in 3D than 2D. 

The scientific community is, however, still defining these cultures. If the 3D sphere becomes too large, the cells on the inside can’t be supported by the nutrients and tend to die. So working out how to handle and optimise these cultures is being developed, and how to assay them. Some people have said in order to assay them, they have to break them apart and place them on plates – so, first we need to have assays that can support a 3D format.

PI: Organ-on-a-chip technology is on the list of top ten emerging technologies – do you think it will support preclinical studies? Is there potential to develop a brain-on-a-chip?

MG: We’ll see, maybe it will be combined with 3D cultures. In terms of modelling the brain, some people have performed 3D cultures and have seen advantages over 2D. Everybody needs to keep in mind that these are models – and depending on what you want to model, some technologies will be more relevant than others. 

Will we ever recreate the human brain or whole body? Unlikely. I definitely think people are using it as a model and applying it. In terms of approval, the FDA will want to see a lot in the form of validation before it is accepted. 

Charlotte Di Salvo, Junior Medical Writer
Proventa International

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Over the last 20 years, fundamental scientific research has been conducted by astronauts aboard the international space station (ISS). It has only been recently that the pharmaceutical and biotech sectors have invested in extraterrestrial experiments, hoping to find novel ways of studying drug behaviour with potential applications on Earth. Merck, AstraZeneca and Sanofi are a few of the pharma giants and small biotechs who hope to “reap the unique benefits of microgravity”.

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Introduction

The unique microgravity environment aboard the ISS creates a useful environment for gaining insights into not only human health, but the structure of molecules. However there are a number of challenges facing pharmaceutical companies should they wish to investigate R&D in space.

Firstly the cost – “getting a single experiment to and back from the ISS can cost some $7.5 million”. For pharma, this is a big issue as they have to perform a cost-benefit analysis with regards to R&D, weighing up the therapeutic benefits and return on investment. In order to encourage private companies to see the value of R&D in space, NASA offers generous subsidies and incentives, although pharma has been slow to leap at the opportunity. 

Secondly, a number of logistical challenges slow the R&D process, which is of equal importance to pharma companies who often rely upon strict timelines to accelerate their drug candidate to market. The small number of astronauts stationed on the ISS and limited time for experimental work plays a big part in this problem. In addition, basic laboratory tasks such as pipetting can be challenging in microgravity. 

As a result, some pharma and biotechs contract companies that specialise in automating scientific experiments. Despite these challenges, a significant number of important contributions have been made to drug development over the last few years thanks to these extraterrestrial experiments. 

Contributions to drug development

Protein crystallisation 

The crystallisation of proteins in space has demonstrated how the microgravity environment can help us gain further insights into structural information about peptides. 

In space, there is no convection, a process by which heat is transferred by movement of a heated fluid such as air or water. On Earth, this causes solutions to flow in any direction due to differences of density. In addition to the absence of convection, there is no precipitation to cause heavier molecules to sink. Therefore, protein molecules form an orderly and a high quality crystal that is beneficial to the study of its structure.

According to a 2019 Nature article, these crystallisation processes have been widely used in the pharmaceutical industry for the manufacture, storage, and delivery of small-molecule and small protein therapeutics. 

It was highlighted that crystallisation processes play an important part in the purification and formulation of small-molecule drugs and peptide therapeutics for oral administration. This offers a significant opportunity for companies in drug development who wish to investigate whether their candidate can be developed in an oral form, typically the most tolerated form of drug administration. 

In addition, these processes have been shown to reduce production cost as well as improve the overall quality and shelf life of the final products.

While crystallisation has been widely used for small molecule and protein therapeutics, the same cannot be said for biologics. A study published in 2019 investigated the possibility of crystallising the monoclonal antibody drug known as Pembrolizumab (Keytruda) in a microgravity experiment. 

The study found that by “leveraging microgravity effects such as reduced sedimentation and minimal convection currents, conditions producing crystalline suspensions of homogeneous monomodal particle size distribution (39 μm) in high yield were identified”. This is a significant finding, especially as the size and flexibility of biologics such as this have previously posed a challenge for crystallisation. 

The results from a number of further experiments in the study helped develop techniques for crystallisation of Pembrolizumab on Earth. The team were successfully developed uniform crystalline formulations with syringeability properties suitable for the preparation of an injectable product. 

The results of these experiments could widen the drug delivery options for biologics not previously thought possible which would help to improve the safety, adherence, and quality of life for patients.

Other developments 

As well as innovations in drug development, scientific experiments in space have contributed to a number of other exciting advancements. On 9 June 2021, a NASA press release described how two research teams successfully created lab-grown human liver tissues that were capable of functioning for 30 days in the lab. 

Competing as teams in NASA’s Vascular Tissue Challenge. Winston and WFIRM each used a different approach to grow human liver tissues that were durable enough to survive and function similar to inside the human body.

According to NASA, “the winning teams used 3D printing technologies to create gel-like molds, or scaffolds, with a network of channels designed to maintain sufficient oxygen and nutrient levels to keep the constructed tissues alive for their 30-day trials”. Developing a vascular system that can sustainably survive for long-periods of time is one of the main challenges within tissue engineering. Hence, the success of emerging technologies like 3D printing in this context may encourage pharmaceutical leaders to invest in this technology. 

In terms of space technology, it was inferred that experiments in microgravity “may facilitate the engineering of even larger and more complex tissues” that more closely mimic function in the human body, in comparison to tissues constructed on Earth. 

In the future, this research could help develop 3D tissue that is capable of surviving in the human body long-term with the same functionality as organic tissue. Eventually, the research and therapeutic applications that develop from advancements such as this could lead to organ bandages and replacements.

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

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The post Drugs in Space: How Extraterrestrial Experiments Help Us Develop Medicine appeared first on Proventa International.

The development of a ‘Findable, Accessible, Interoperable and Reusable’ (FAIR) data approach has been an exciting step towards an information-centric approach for the pharmaceutical industry. The potential benefits of this alternative approach to data management and accessibility could significantly support pharmaceutical R&D and drug development. However, adopting this new approach could be challenging for some organisations and may not be as popular as anticipated. 

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Introduction to FAIR data

Two of the main problems limiting drug development and clinical research are the accessibility and management of data. There are vast genetic databases, medical records and scientific archives which have the potential to support the pharmaceutical industry but have only just begun to be utilised efficiently.

FAIR data is a recently developed concept built on a number of principles which aim to support drug discovery through good data management, which are as follows:

• Findable – Data are richly described by metadata and have a unique and persistent identifier

• Accessible – Data and corresponding metadata are understandable to humans and machines, and accessible through defined protocols

• Interoperable – Data and corresponding metadata use formal and accessible knowledge representation to guarantee reuse

• Reusable – Metadata accurately describe the provenance and usage license for the data

Findable is one of the most important principles for FAIR data and is built on the premise that data must be of high quality when it is inputted into the database. In other words, up until its most recent update, data must be meta-tagged to ensure the record of the data is up-to-date and accurate. This includes “information on the author, those that have access to the data, who has modified it and a whole series of characteristics such as category, security classification and file type”. 

This metadata is critical for intelligent databases to filter through vast datasets to retrieve relevant records.

The ultimate goal of FAIR data is to help lower R&D costs, reduce drug development timelines and prevent late-stage failures. Extensive analysis of data across multiple platforms could help researchers to better understand a disease and potentially modify preclinical models to better assess drug pharmacokinetics in a biological system. 

Challenges

The principles for FAIR data helps users to get the most out of the available data, however implementation in the pharmaceutical industry has been slow for two main reasons, according to a publication.

Firstly, the implementation of FAIR data is not necessarily pre-determined or standardised. FAIRification is a long-term solution of how data is created and used within an organisation, however this concept continues to change as our knowledge of processes (biological and digital) evolves. 

This was a point emphasised in the aforementioned publication which states that a critical part of FAIRification is the mapping of data according to a semantic model. “This model describes the meaning and relevance of each data point, and its relationships to other data in the context of a knowledge domain. Gaps in our understanding of biology make this mapping a moving target. New information fills in what we don’t know and changes what we thought we did know.”

The second reason contributing to slow implementation is the potential cultural implications. For those in the pharmaceutical industry who have always worked in information silos, shifting to sharing and reusing data may be considered counterintuitive. Hence, adopting the FAIR principles may be a challenging and arduous process for many organisations. 

Other challenges raised include the need for “clear data licensing, how to proceed and what license to suggest/apply to data from a wide variety of sources (governments, agencies, states, universities, investigators, etc)” and how the need for user identification for access to platforms may introduce a new barrier to data access. 

Importance of FAIR data

The implementation of FAIRification across the pharma industry has potential to support everything from R&D to drug development. According to a recent Nature article, the following stakeholders are those who could benefit the most:

• Researchers wanting to share, get credit, and reuse each other’s data and interpretations

• Professional data publishers offering their services

• Software and tool-builders providing data analysis and processing services such as reusable workflows

• Funding agencies (private and public) increasingly concerned with long-term data stewardship

• Data science community mining, integrating and analysing new and existing data to advance discovery. 

Improving the quality, accessibility and management of data sources across the industry will become a valuable resource for many organisations. Not only could FAIR data be used to address the challenges of long development timelines, but also prevent problems arising in the future, thanks to the shared knowledge of others. 

Drug development relies so heavily on participant information and the findings from previous trials which emphasises the importance of re-usable data to significantly cut down the cost and time of R&D, ultimately shortening the time to market for new and improved drugs. 

The concept of reusing and improving accessibility to shared data would also allow pharma organisations to respond more rapidly and effectively to changes in business demands – in comparison to information silos and generating new data which is time consuming and costly. 

Example of FAIRness 

Open PHACTS is a data integration platform for information pertaining to drug discovery. Using a machine-accessible interface, users can access the platform which provides human and machine-readable representations.  

One of many uses for Open PHACTS is obtaining gene names correlated with UniProt identifiers. UniProt is a freely accessible, online database of protein sequences and functional information. The related proteins retrieved from these methods may represent splice variants, orthologues or homologous paralogues.

The platform draws together multiple sources of publicly-available biomolecular, pharmacological and physicochemical data which can respond to “structured, well defined queries in a meaningful and reproducible way”. 

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

The post Building a FAIR Culture in Pharma appeared first on Proventa International.

From artificial neural networks to predictive modelling, artificial intelligence (AI) is making its mark in the pharmaceutical industry. The ever-increasing cost of development, shorter timelines and rise in demand has seen the industry reach out to AI to help bring drugs to market faster and cheaper. Machine learning (ML) is one branch of AI with exciting applications in drug discovery. 

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The cost of bringing a drug to market continues to increase at an exponential rate. According to a recent analysis, between 2009 and 2018, US biopharmaceutical companies spent approximately $1 billion bringing each new drug to market. The majority of large expenses occur during the early phases of drug development in drug discovery

Hear from some of the industry leaders including Huijun Wang – who will be providing her expertise in leading a discussion Expediting the Drug Discovery Process Through Chemistry and Biology: Leveraging AI in Hit Finding and Lead Optimisation. To discuss these innovations and more with other leading experts in an informal setting, sign up to Proventa’s Medicinal Chemistry and Biology Strategy Meetings, held online on 29 June 2021.  

Drug discovery 

Human dose production is a particularly challenging area, in which conventional in vitro modelling faces a dilemma with poor translatability. This often results in early termination of clinical trials when drugs do not demonstrate the same pharmacokinetics within humans as predicted preclinically. 

It appears that ML, a branch of AI, is leading the way in the latest innovations. A recent example was a 2021 study which used ML attempts for predicting human subcutaneous bioavailability of monoclonal antibodies. The measured bioavailability of the monoclonal antibodies ranged from 35% to 90%. The decision tree-based method, a form of ML, proved to best predict bioavailability. 

Since all of the ML approaches used theoretical calculations and predictions for input, it was suggested from the study that these models may be most useful for early-stage activities like molecule formational design. 

A form of AI known as natural language processing (NLP), can be used to optimise the process of target identification. NLP extracts “meaning from human language to make decisions based on the information”. This can be used to scan vast numbers of publications and genetic databases to search for gene-disease associations and identify new targets. The AI-based algorithms can perform tasks such as this with greater accuracy and speed in comparison with human intelligence. 

The importance of prioritising the most potent compounds for a relevant therapeutic target is emphasised in a 2018 study investigating machine learning for predicting drug-target interactions.

In the publication, it is emphasised that hit identification “is the first step towards new drug development. Identifying unexpected off-targets can open the possibility of drug repurposing or can lead to insights for predicting and explaining observed side-effects.”

Machine learning on DNA-encoded libraries

DNA-encoded libraries (DELs) have been increasingly explored in recent years to enhance hit identification in drug discovery. DELs represent a modern and versatile tool used to better identify a greater range of novel biological compounds. These libraries are capable of screening drug targets with an extensive number of compounds with great efficiency.

Unfortunately, in order to analyse vast amounts of data, DELs are still dependent on bioinformatics operated by humans. The result of this “limits the scale of molecules considered, introduces bias, and makes it difficult to fully utilise the subtle patterns in the DEL selections”.

Utilising ML allows the identification of important features and obvious patterns from a small dataset and uses the information to create projections for larger datasets. In a recent study, two types of ML models were trained on the DEL selection data to classify compounds: random forest and graph convolutional neural network (GCNN). 

The random forest is an algorithm that creates a predictive model comprising a large number of individual ‘decision trees’ which operate as a whole group. Each tree in the forest produces a class prediction and the class with the most votes becomes the model’s prediction. 

These methods have already demonstrated success in a study which reported that ML models verified hits up to 29% at one micromolar. The ability to identify target molecules on a micro scale is critical for creating a larger hit pool.

GCNN is a form of ML known as deep learning. One of the main benefits for the application of GCNN for DEL is that “deep learning methods automatically extract important features from a dataset whereas manually generated features are necessary for conventional machine learning algorithms”. Therefore, GCNN is more likely to identify potential hits faster and with greater accuracy than DEL alone. 

Iterative screening

High throughput screening (HTS) is the most popular approach for screening large libraries of compounds against a target of interest. Unfortunately, the sheer size of these libraries results in a high screening cost to run. In addition, the low hit rate of HTS, typically less than 1% in most assays, requires large compound libraries to generate a sufficient number of hits for drug development programs to progress.  

Iterative screening is a process in which drug screening is performed in batches – each batch is filled by using ML to select the most promising compounds from the library based on the previous results. Iterative screening has been shown to enhance the efficiency of HTS, as it allows for a smaller part of the library to be screened at a time, while still identifying a large portion of the active compounds.

Previously, an iterative approach to HTS was considered impractical due to the high labour costs, however “advances in screening automation have made custom selection of compounds more broadly feasible”.

A recent study investigated the iterative approach to HTS, and found that “the hit rate in the iterative screening was just greater than twice that of normal (random) screening, recovering a median of 78% of the active compounds when 35% of the library had been screened”. 

It is worth noting that there are a number of potential practical challenges that can arise with iterative drug screening. While the iterative approach to screening increases the rate of hit identification, the overall process can be “resource intensive and the interim analysis of screening data will potentially require more time for quality control and data management”. These are considerations that will need to be taken into account when weighing up the value of hit identification and whether these challenges can be overcome in the future. 

The results from this study however, remain positive and demonstrate how ML approaches like iterative screening show potential to optimise drug discovery. 

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Medicinal Chemistry and Biology Strategy Meeting, set for 29 June 2021.

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

For more articles covering the pharmaceutical industry, clinical research and academia, visit our content site PharmaFeatures.

The post Utilising Machine Learning in Drug Discovery: Opportunities and Challenges appeared first on Proventa International.