In 2003, researchers sequenced approximately 92% of the human genome, a huge achievement and very recently researchers have completed the entire process. Now, the latest innovation in AI technology has predicted the structure of nearly the entire human proteome. The impact of this on drug discovery advancements could open the door to previously undruggable targets and a host of therapeutic breakthroughs. 

The AI machinery responsible for this historical news is known as AlphaFold, developed by Google’s sister company DeepMind in London. The human genome codes for the human proteome, the full complement of proteins expressed by an organism. However, up until now, only a third of the 3D structures for the proteome have been determined. 

Now, AlphaFold has been able to characterise the structure of more than 35,000 proteins (which vary in accuracy) available through a public database. According to a Nature publication, the database is said to grow to 130 million structures by the end of 2021 and has aimed to predict the structure of every protein in humans as well as 20 model organisms. 

The DeepMind Programme has proven its value after outperforming approx 100 other teams in a protein-structure prediction challenge called CASP – Critical Assessment of Structure Prediction.

The protein folding problem

Proteins constitute one of the key areas of focus for therapeutic targets, especially so in the last few years, with research investigating protein-protein interactions and targeted protein degradation. Unfortunately, this research has been limited in progress by something known as the ‘protein folding problem’.

A historical hypothesis inferred that, in theory, a protein’s amino acid sequence should fully determine its structure. The challenge is that it has been impossible to characterise protein structure due to the vast number of confirmations it could fold into before settling into the final 3D structure. 

A folded protein can be thought of as a “spatial graph” which is important for understanding the physical interactions within proteins as well as their evolutionary history. According to DeepMind, AlphaFold works by creating an “attention-based neural network system, trained end-to-end, that attempts to interpret the structure of this graph. It uses evolutionarily related sequences, multiple sequence alignment, and a representation of amino acid residue pairs to refine this graph.”

A neural network system is a form of deep learning, which is developed from a branch of AI called machine learning (ML). Deep learning is a specialised area of ML that attempts to model abstraction from large-scale data using multi-layered deep neural networks (DNNs). Abstraction is a computer science term that refers to the process of filtering out irrelevant data in order to focus on the desired information. The neural network structure aims to mimic how the human brain calculates problems and analyses data through a set of algorithms.

Through this complex process, AlphaFold develops strong predictions of the underlying physical protein structure, which it can determine highly accurate structures of in the space of a few days. 

AlphaFold has been trained upon publicly available data of approximately 170,000 protein structures and large databases containing the sequences of proteins with unknown structures.  

How will this contribute to therapeutic advancements?

DeepMind is optimistic about the impact of AlphaFold on biological research, especially in terms of understanding disease pathology. Understanding the 3D structure of a protein is so important as it plays a critical role in their function and contribution to physiological changes in the body. 

Genes determine the amino acid sequence which determines the final structure of the protein – hence, an error in the genetic code may result in the malformation of a protein, causing disease or death. 

The link between protein malformation and disease is not a new concept, however, targeting proteins has been an uphill struggle due to the fact that the final 3D structure of many proteins remains unknown. Knowing the genetic code of a protein is not enough – the structure is the key for drug targeting. 

It’s not to say however that scientists have failed to determine protein structure – experimental techniques like X-Ray crystallography have been used over the last few decades to successfully determine protein shape. Unfortunately, these methods can take years to perform, cost thousands of dollars per protein structure and depend a lot on trial and error. 

In terms of contributing to therapeutic advancements, DeepLearning could help accelerate research by predicting a protein’s shape computationally from its genetic code alone, rather than timely laborious lab work involving techniques like X-Ray crystallography.

Even more interesting is that “some of the regions that AlphaFold predicted with low confidence match up with those that biologists suspect are disordered” – a quote from the Head of AI for science at DeepMind. This is the first step forward for researchers to begin to understand how protein structure contributes to specific diseases, by understanding more about the complex structure in detail. In other words, AlphaFold could help to identify proteins which have malfunction and provide more information about how they interact. 

These insights would no doubt contribute to more precise targeting in drug development, should researchers identify the structure of malfunctioned proteins and target specific regions of interest. 

Recent applications: COVID-19

In early 2020, AlphaFold predicted several protein structures of SARS-CoV-2 virus – ORF3a and ORF8 – which were previously unknown. Recent work by experimentalists have confirmed the structure of both proteins, supporting the accuracy of AlphaFold’s protein predictions. This is a significant achievement especially given the challenging nature of the proteins with very few related sequences for the AI system to utilise. 

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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Despite almost two thirds of the adult population in the UK having been vaccinated, cases are on the rise, which appear to be associated with the highly-transmissible delta variant of COVID-19. According to The European Centre for Disease Prevention and Control, it has estimated that the Delta variant will account for 90% of strains in circulation in the European Union by the end of August. With fears this could lead to a third wave in winter, countries across the globe are beginning to launch booster shot studies campaigns to protect the most vulnerable in the coming months. 

The COVID-19 booster shot will work in a similar fashion to the seasonal flu booster. Like COVID-19, the influenza virus has a tendency to mutate rapidly. Seasonal flu shots protect against the “three or four influenza viruses that research suggests may be most common during the upcoming season”. 

In terms of what these boosters comprise, that is one of the topics for debate. 

According to a publication in The Times, the UK government is in talks with AstraZeneca about the development of booster shots which specifically target the variants of concern in the population. Whether this has been confirmed or not, or if the shots would be ready for the vaccination season in autumn is another question.

Ongoing studies are taking an alternative approach, investigating the efficacy of mixing doses from different pharmaceutical companies. The world-first COVID-19 vaccine booster study launched in the UK in May 2021, investigating the safety and efficacy of booster shots for COVID-19 protection in winter. 

According to the government, the Com-COV trial will look at “seven different COVID-19 vaccines as potential boosters, given at least 10 to 12 weeks after a second dose as part of the ongoing vaccination programme. One booster will be provided to each volunteer and could be a different brand to the one they were originally vaccinated with. Vaccines being trialled include Oxford/AstraZeneca, Pfizer/BioNTech, Moderna, Novavax, Valneva, Janssen and Curevac, as well as a control group”.

Last month the study found that a shot of Pfizer’s vaccine administered four weeks after an AstraZeneca shot will produce a better response than a double dose of AstraZeneca. However, there is a level of uncertainty with regards to this regime across the UK population. In a recent article, England’s Deputy Chief Medical Officer Jonathan Van-Tam said that “given the UK’s stable supply position there is no reason to change vaccine schedules at this moment in time.”

The pharmaceutical debate

Since early July, a number of pharma companies have been pushing for the launch of booster shots in time for the autumn vaccination season. In a statement published last week, both Pfizer and BioNTech stated that real-world data showed a decline in protection against symptomatic infection six months after vaccination, despite the vaccine still providing protection against severe cases of illness. 

Israel recently became the first country to offer third doses of the BioNTech/Pfizer shot as boosters for adults with serious, pre-existing medical conditions. 

Pfizer is currently in the process of applying for Emergency Use Authorization in August from the FDA to administer third doses. However, this proposal was not met with agreement and instead was faced with a clear opposition by the FDA and CDC. A joint statement by the FDA and CDC was released on 8 July  2021 stating that “Americans who have been fully vaccinated do not need a booster shot at this time. FDA, CDC, and NIH are engaged in a science-based, rigorous process to consider whether or when a booster might be necessary.”

The view of the CDC appears to be built on the premise that booster campaigns will only be launched should the data warrant it i.e.“once there’s a demonstrated decline in efficacy, not just a waning antibody response”, according to a statement by CDC experts in a pharma publication. 

The consensus across regulatory bodies, academia and some governments is that there is very little evidence to suggest the rapid implementation of COVID-19 booster shot programmes. 

In a recent article, Ann Lindstrand, WHO Covax co-lead, explained that currently there isn’t enough evidence pointing to a need for booster doses. In addition, WHO Chief Scientist Dr. Soumiya Swaminathan, “reminded that decisions about booster doses should be made based on data, and not “on companies saying we need a third dose.”

Other concerns regarding the booster shots is whether they introduce further potential side effects i.e. do the risks outweigh the benefits, and is there enough of a drop in efficacy from vaccines to support the potential side effects? 

The UK Com-COV trial investigating the mixing of doses for boosters found the vaccines “incurred more frequent reactions than those on standard non-mixed schedules. Some of the mild to moderate symptoms reported among participants receiving a mixed vaccine schedule included chills, fatigue, feverishness, headache, joint pain, malaise, muscle ache and pain at the injection site.”

It is worth noting that adverse effects were mild and relatively short-lived, and future booster shots may not necessarily be mixed as the study is a preliminary investigation. However, there is increasing concern that the side-effects of a third dose could severely affect clinically vulnerable patients.

Ethical considerations

Another hurdle for pharma companies hoping to push booster shots is the ethical consideration of offering third doses to first-world countries like the US and UK before third-world countries who haven’t yet had access to a first. According to the WHO, almost half of the US population is fully vaccinated in comparison to Africa which has seen less than 2% of the total population receive both doses. 

Pharma companies like Pfizer continue to demonstrate their efforts in addressing this equality gap. In a press release from 10 June, 2021, Pfizer and BioNTech announced plans to provide 500 million doses of COVID-19 vaccines to the US government to donate to the poorest nations. 

However, statements across the media echo the ethical responsibility to vaccinate the global population. A recent article emphasises this point with a bold statement from the Director of Global Health in Emergency Medicine at New York-Presbyterian/Columbia University Medical Center. Bluntly put, he tweeted that “No matter what pharmaceutical companies tell you, you don’t need a Covid-19 booster more urgently than all the healthcare workers around the world who haven’t yet had access to a vaccine”.

Pharma companies continue to support the roll out of vaccination programmes across the globe to allow every individual to receive the vaccine. Not only is this important from an ethical standpoint; from a scientific perspective, global vaccination will no doubt play an important part of controlling the spread of the delta variant and hopefully prevent further mutations arising. 

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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While the impact of COVID-19 was significant in every area of the pharmaceutical sector, some of the most pandemic’s most overlooked impacts were within the regulatory space. From study design to submissions, the virus affected every aspect of drug regulation, from trial design and co-ordination to submission of documents and filings. 

Redesigning clinical trials

The impact of COVID-19 required a more prioritised, rapid vaccine development than has ever been seen before. The need to push through an efficacious, easily manufacturable product to combat COVID affected every aspect of the pharma industry, from drug design to trials to manufacturing and regulatory affairs.

Rapid developments in the regulatory space have been critical in speeding up the clinical trial process. The need to ensure patient safety led to a decentralised, fluid approach to clinical trials, which in turn saw an uptick in ways patient data can be harvested. The urgent need for swift approvals meant that standard procedure was modified or relaxed, with regulatory applications for vaccines approved in less than a week. Overall, regulatory changes made during COVID reduced approval timelines by three to four months. 

The increased speed of the drug development process has also meant that companies need more rapid access to regulatory authorities in order to quickly assess clinical trial development plans or changes. In light of the pandemic, grants of pre-investigational new drug (IND) meetings with the FDA were given in under 30 days, with a reduction in the specificity of non-clinical information needed in order to start studies more quickly.

Another major regulatory change to speed up trial completion was the move towards centralised site clearance. This move away from local approvals of each trial site allowed for quicker approval of sites and subsequently a shortened approval timeline, from months to only days. 

Beyond these changes, regulators became more flexible generally regarding trial design and product approval. A more flexible approach was created regarding product registration, rapid review of trials and auditing. Deadlines were extended where necessary. 

Decentralisation

Novel technologies and the need for social distancing saw the rapid uptake of decentralised trials, often taking place in patients’ homes. These trials have been shown to cost at most the same as traditional trials, but with the added benefits of increased patient engagement and retention, greater data collection and overall faster, more flexible study timelines. 

Scaling up decentralisation while maintaining quality will, however, mean the need for much more rigorous compliance given the diversification of data sources and complexity of large-scale studies. Automation is one solution to this increased need for vigilance, with unmanned data capture through wearable devices and automated reporting critical to ensuring accuracy and completeness of translatable submissions.

The new methods of collecting patient data – from wearables like the Apple Watch to electronic forms such as eCOA – were rapidly accepted and incorporated by regulators, and have helped increase patient safety in trials as well as facilitating a more rapid turnaround in trial completion. 

Regulatory submissions

Shortly after the scale of the pandemic became clear, regulatory authorities began granting Emergency Use Authorizations (EUAs) to COVID treatments. This meant that the onus of creating efficiencies to speed up drug approval fell on regulators. In part, this came from innovations in technology and digitalisation.

Regulatory information management (RIM) technology was used widely to reduce reporting activities through automation and machine learning. Other new approaches to reporting could rapidly expand this speed and efficiency, replacing older models of reporting: adoption of electronic Common Technical Documents, use of electronic product labels, reform to the Certificate of a Pharmaceutical Product procedures, and discarding the need for ‘wet signatures’ on documents would all significantly reduce the current time and onerousness of regulatory reporting for a number of countries and businesses. 

The future of pharma regulation 

The major changes witnessed during the pandemic were created in an extreme situation, and it is without doubt that their extremity will be reduced as the world returns to normal. Nevertheless, many of the lessons learnt during the COVID-19 crisis will continue to be preserved, from swifter pre-IND meeting times to safer drug trial practices. 

Real-world evidence (RWE) will be a major regulatory force in the coming years, with real-time access and analysis of data providing a huge boon to drug research – as long as regulators can fully standardise datasets and analytics, as a lack of careful monitoring can lead to potential risks in the procedure. 

The technology that has been pioneered during the pandemic will continue to grow and develop as COVID-19 recedes into memory. Cloud technology will allow greater connectivity between teams and reduced siloing, on top of speeding up regulatory submission significantly, as soon as it is taken up by regulatory agencies globally. AI, automation and machine learning will speed up document completion and regulatory submission, and move the industry towards higher-value innovations. 

Joshua Neil, Editor
Proventa International

The post COVID’s Impact on Innovations in Regulatory Affairs appeared first on Proventa International.

Until now, patients with advanced cervical cancer had relatively low survival rates. The recent approval of Keytruda, a PD-1/PD-L1 checkpoint inhibitor, is the latest breakthrough for cervical cancer immunotherapy. The positive outcome from Merck & Co’s clinical trial now offers hope for the future of treatment which can extend the lives for stage 3 and stage 4 cervical cancer patients. 

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The latest news

Merck’s Keytruda has recently become the first checkpoint inhibitor to help patients with advanced cervical cancer live longer when used alongside standard first-line drugs. Keytruda is currently the only PD-1/PD-L1 inhibitor approved for advanced cervical cancer.

This is huge news for both researchers and patients. Currently, the five-year survival rate for stage 3 and 4 cervical cancer is around 40% and 15% respectively, according to CRUK. The detrimental drop in survival rates emphasises an unmet clinical need for late-stage cervical cancer drugs.  

In addition to hope for patients with advanced cervical cancer, the news of Keytruda achieving approval represents an important step in cancer treatment. Stage 4 cancer especially is difficult to control due to the wide spread across the body through metastasis. If patients with advanced cervical cancer can prolong their life with this drug, there is potential to investigate the same for other advanced cancer types. 

Immunotherapy – checkpoint inhibitors 

Immune checkpoints are components within the immune system which prevent healthy cells from being destroyed by a strong immune response. The inhibition of immune checkpoint molecules such as programmed cell death 1 (PD-1) have shown great efficacy across many cancer types. 

PD-1 plays a critical role in inhibiting the immune response that would otherwise attack the cancer cells and promotes “self-tolerance through modulating the activity of T-cells”. Programmed Cell Death Ligand (PD-L1) is a transmembrane protein which can combine with PD-1 and is considered a co-inhibitory factor within this response. 

Inhibition of these checkpoints aims to prevent an “off” signal from being transmitted within the immune system which would otherwise prevent T-cells from destroying cancer cells.

Accumulating evidence indicates the inhibition of PD-1 supports the immune response against cancer cells. According to a 2020 publication, PD-1 signalling pathway suppression has shown that “the clinical response of patients with different solid tumors and hematological malignancies, mainly relies on T-cells effectively to penetrate the tumor”.

The efficacy of checkpoint inhibition is supported by the increasing number of clinical studies which highlight significant clinical response in a wide range of cancer patients as a result of PD-L1 targeted therapies. 

An example of this success was the approval of Atezolizumab, the first FDA-approved PD-L1 inhibitor.

Atezolizumab is a humanised, anti-PD-L1 monoclonal antibody. These antibodies are engineered within the lab to mimic human-derived antibodies to enhance the immune response.

The drug is used as a first-line combinatorial therapy used to treat six types of cancer, including triple negative breast cancer and melanoma. Atezolizumab is also under investigation for other types of cancer. 

Side effects 

Due to the wide range of uses for Atezolizumab, a number of studies are investigating the short-term and long-term effects of checkpoint inhibitors. Immune-related adverse effects (irAEs) are a common part of treatment with checkpoint inhibitors, although range in severity and persistence with different drugs. 

In comparison with anti-CTLA-4 antibodies, irAEs related to anti-PD-1 antibodies are less frequent and differ in their spectrum of organ involvement. “Approximately 10% of patients receiving anti-PD-1 antibodies have grade ≥3 irAEs”.  

According to the National Cancer Institute, a recent study investigated the medical records of a cohort with advanced melanoma who had undergone surgery followed by treatment with a checkpoint inhibitor. 

In terms of short-term side effects, a number of immune-related problems arose during treatment. The most common short-term effects were “skin rash or itchy skin, inflammation of the thyroid (thyroiditis) or low thyroid hormones (hypothyroidism), and joint pain.

167 patients (43%) experienced immune-related side effects that lasted for at least three months after completing the immune checkpoint inhibitor. 

The vast majority of these long-term side effects were mild, meaning they interfered somewhat with the patient’s daily activities and may have required treatment. It is worth noting that the majority of the long-term side effects didn’t go away during the study. 

Further studies are underway to gain a better understanding of how these side effects arise and how long they will last to determine the best way to treat or control them.

Future direction for research 

Recently, small nucleotide molecules called microRNAs have become a focus for attention in the targeting of PD-1/PD-L1 in cancer. Extensive data from a large number of studies suggest the role of miRNAs as tumour suppressors, carcinogenic, diagnostic and prognostic biomarkers in lung cancer. 

Recent studies have demonstrated that miR-33a, a form of microRNA, can regulate the expression of immune markers. An early study in 2017 found an association between the level of expression of PD-1/PD-L1 and miR-33a, and found that miR-33a was negatively correlated with the expression of PD-1/PD-L1. 

Long non-coding RNAs (incRNAs) are also an area of interest, with recent data reporting the role of lncRNAs in the modulation of the innate immune response. Results from a 2018 preclinical study demonstrated the efficacy of combination therapy using lncRNA UCA1-targeted therapy and PD-1 immune checkpoint inhibition which was shown to support this combination for the clinical treatment of bladder cancer.

While these areas remain at the preclinical stage for now, the speed of cancer research and demand for effective immunotherapy may accelerate these areas through to clinical trials sooner than anticipated.

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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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. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

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.

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A trio of lawsuits are being levelled at Emisphere Technologies over its recent sale to Novo Nordisk in late 2020. The sale, which saw Emisphere acquired for $1.8 billion, drew criticism from a number of stockholders who alleged that controlling stockholder MHR Fund Management ‘co-opted’ the company’s board and forced an ‘ill-timed’ sale at an ‘artificially low price’. 

Emisphere Technologies’ oral formulation technology, Eligen SNAC, was used in Novo Nordisk’s diabetes medication Rybelsus. The drug launched in September 2019 after receiving FDA approval. In 2020 the drug generated around $305 million.

Rybelsus controls blood sugar levels in adults with type 2 diabetes. Its active substance, semaglutide, increases the amount of insulin that the pancreas releases in response to food. 

Shortly after Rybelsus’ launch, Novo Nordisk announced its purchase of the company, alongside plans to ‘apply and further develop the technology and use it on current and future pipeline assets’. 

 One of those suing Emisphere, IsZo Capital, alleges MHR ‘diverted… a portion of the purchase price to its pocket’, and that MHR Chief Investment Officer Mark Rachesky ceded the majority-of-the-minority condition during the sale talks. The lawsuit also claims that Emisphere was worth considerably more than the $1.35 billion remaining for stockholders after MHR’s payout is accounted for. 

Finally, the lawsuits allege that compensation given to two MHR appointees for nine month’s work – each receiving over $7.8 million – should not have happened to the extent that it did.

Joshua Neil, Editor
Proventa International

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Mental health conditions like addiction and diseases like Alzheimer’s represent a great unmet clinical need. Psychedelic research at preclinical stages have shown potential for positive neurological changes in the brain for models of depression, addiction and Alzheimer’s. In a recent interview with Proventa International, Assistant Professor Albert Garcia-Romeu, from the Johns Hopkins Center for Psychedelic and Consciousness Research, discussed the benefits and challenges of psychedelic research.

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Proventa: Would you mind introducing your background in the field and your personal targets for research?

Albert Garcia-Romeu: I am an assistant professor at John Hopkins School of Medicine where we have established a Center for Psychedelic and Consciousness Research. A lot of my research experience here has focused on studying the effects of psilocybin in various populations. We are particularly interested in how psilocybin can be potentially used in a therapeutic fashion – for instance in treating conditions such as tobacco addiction. Another area I’ve been working on more recently has been examining psilocybin in patients with early-stage Alzheimer’s disease. 

These are the two areas I’ve mostly focused on for the last nine years. 

How translatable do you believe the preclinical models of psychedelic drugs are to the human brain?

AGR: For some things [animal models] are very good and useful. But when we’re talking about people and their experiences, I don’t think it translates as well – you often have to coax animals to become addicted to drugs over time in a small environment with little stimuli. People, on the other hand, have to deal with other stimuli like social interactions and personal issues that don’t necessarily translate to animal models. 

For example, with rodent models of depression, how do we know whether rodent behaviour is translatable to how a human feels when they are depressed? Or when a person has lost their job or a loved one?

[Animal models] are very useful for looking at molecular changes, e.g. in parts of the brain or circuits or synapses. It can provide a window into some of these higher level changes – though I think some of the higher level functions like sense of self may not be fully translatable. If you look at medication development, everyone has heard a number of times about drugs which might restore memory in mouse models of Alzheimer’s, but don’t work at all in the transition to human trials. We are a lot more complicated than animals – although animals are still pretty complicated. 

They [animal models] definitely give us great insights that we wouldn’t necessarily be able to achieve from human work, however.

Could you describe the mechanism as to how psychedelic compounds like psilocybin could help patients with MDD for example, or an addiction?

AGR: We don’t exactly know the answer to the first part of that question. We know these drugs have been used for thousands of years by people – so what we do know is they are psychoactive and they create these very powerful mood-altering effects, altering our sensory perception, sense of self, and perception of time. These features are consistent with use in ceremonial or spiritual practices among indigenous cultures. 

More recently, psychedelics were used within the counterculture in the 1960s. At the time, people called these drug experiences “mind-expanding”. So psychologically, the experiences people undergo on these drugs often reflect the desired effects we might seek from conventional therapy like generating insight and getting new perspectives. These experiences can change the way we think about ourselves, our lives. 

At our laboratory, we use primarily high doses of psilocybin to create very powerful experiences that can promote psychological and behavioural changes. Sometimes these experiences can have existential or spiritual overtones and this can help people reflect and look back at recurring patterns in their lives and mental landscapes. This is more on the psychological side, but we have also seen a great uptick in research and science investigating how these drugs are altering brain function when people are on these drugs. 

What’s very odd about the psychedelics however, is that they also have an ongoing persistent signal which can occur a day later, a week later, a month later – showing that the brain is still functioning differently than before the drug was taken. 

And so that seems to be associated with some form of mental reset, the experiences people have are changing the way they process emotions. For instance, the activity of the amygdala has been shown to change after psilocybin, which has also been linked to antidepressant effects and positive emotions. These are some of the neurobiological mechanisms that we are starting to learn more about. In terms of the neuroscience, we are only just starting to scratch the surface of both cellular changes that are happening in the brain, such as increased synapse formation in areas like the prefrontal cortex and the hippocampus, and epigenetic changes that seem to occur after a single dose of LSD or psilocybin. 

Again those are data not necessarily based on human research but looking at animal models, and cellular/molecular systems. However, they’re giving us more information as to what’s happening at the smaller units of the brain which could be fostering these long-lasting changes. 

One of the problems with current treatment for psychological problems like depression is that prescribed drugs are typically a short-term option – is this something you believe psychedelic drugs could overcome, inducing more long-lasting changes in the brain?

AGR: We hope so, but we can’t say that for sure. The weird thing about people is that we’re not this passive structure like a puzzle. We’re constantly re-building our own little puzzles, moving things around – the behaviours we undertake, the way we think about the world, whatever we’re putting into our system is constantly pushing our bodies and brains to re-write themselves. 

So I think that with supportive therapy combined with psilocybin, we have the ability to help a person communicate where they’re at, where they’d like to be going, where the roadblocks are. And the drug experiences themselves can provide a catalyst to make them feel and think differently in the moment, but then to support that in the longer term by actively attempting to make these changes in the way we’re thinking, the way we relate to ourselves and others, the way we are behaving in the world, which can be further encouraged with careful integration after the drug effects wear off.

And so when we do that, we provide a scaffolding, if you will, to allow the brain to become more permanently re-wired. This is a little different to the model treatment-wise from what you would see with standard antidepressants, and certainly the experiences induced by the drugs are very different when comparing psilocybin with other medications. 

What considerations should be taken into account if these drugs reach the market, with regards to the age of those using them (especially so if permanent changes in the brain occur)?

AGR: That’s a good question. I don’t really know exactly where regulators will draw the line there. Drugs like these would initially be confined to adults at the start and then you could possibly move into younger patients like adolescents eventually, though I think this will have to be done very carefully, and I don’t foresee this happening anytime soon. 

I think for now we’re squarely focused on adults – 18 or 21 years old are the usual age cutoffs in research. One of the areas that needs to be addressed if these drugs reach the market, is how to approach studies to ensure people have these experiences safely and ensure there is appropriate training of people monitoring these experiences and managing them therapeutically. 

How significantly will regulatory restrictions impact research and production of psychedelic drugs?

AGR: I mean it’s made this a very slow process. The lab I’m working at has been doing this for two decades and people weren’t really doing this in the US before that very much because of all these restrictions. Looking 30, 40 years ago, there was almost no research in this area because of regulatory restrictions.

It’s been a hurdle that’s been very difficult to overcome, but I think the regulatory bodies in the US at least are trying to encourage research that has potential for therapeutic development. And again it becomes complicated when companies try to patent what is a natural substance like psilocybin, so then people have to figure out how to create this in a marketable way to get some sort of ROI and that becomes a whole other problem. 

In your opinion, why do you think psychedelic research is receiving so much attention and investment? Is it the potential to address some of the challenges with current treatment?

AGR: You know, I think science is very much fad-driven. One area will be fashionable while another is not. There’s been huge interest, and obviously a lot of people want to try to catch that gravy train and make money. 

Part of the problem is our culture and society have developed in ways that can be unhealthy for us mentally, and in many ways, what we’ve been using have been more stop-gap treatments. The types of medication that we have don’t work for everyone and have side effects. Now we’re seeing people that want to turn psychedelic medicines into a profitable venture. We’ve seen this with cannabis – as soon as legalistion occurred, you had people turning up saying, “well this is legal now so we’re going to grow it and sell it to you for a profit” and so it’s a weird kind of dynamic to see that happening at a very rapid rate with psychedelics. 

There may be a time when we can source psilocybin in the field, people could grow it in their gardens at home so we don’t necessarily need some manufacturer selling us the pills. But the next sort of arms race will be people using machine learning algorithms and AI to try to build molecules that they say “this is going to work better than psilocybin”, or “this acts faster, so you don’t have to sit there for six to eight hours, you can get the same benefit in 15 minutes”, or “my formulation is so good because it still produces what we see with psilocybin i.e. healthy synaptic changes but it doesn’t have any psychedelic action, so you can have it while sitting at your desk at work”. It’s all a way to better market it, but we’ll have to see how the actual clinical research turns out in the end.

My take is that psychedelic treatments can really help us fill an important need though, which is why we’re seeing so much interest. I think these substances can help us to spend more time on our own internal experiences, processing difficult emotions like grief or trauma that we’ve swept under the carpet, and working with a therapist to figure out how that plays into our larger life. And this stands in contrast to more standard available medications, where the drugs can help treat the symptoms, but not always the underlying cause. So, I hope this can lead to a larger shift in our mental healthcare systems that lead to better care overall.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

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Every hour, two more people are diagnosed with Parkinson’s in the UK and the global patient population continues to rise. To date, there are no disease-modifying therapies available on the market for Parkinson’s (PD) patients. Pharmaceutical company Bayer is simultaneously launching stem cell therapy and gene therapy trials for Parkinson’s patients. The aim of this is to reverse the decline in motor control caused by the disease.

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Introduction

Parkinson’s is a progressive neurodegenerative disease in which depletion of the dopamine neurons in a region known as the substantia nigra causes a multitude of symptoms. The most notable of these symptoms include tremors, slowed movement, memory difficulties and behavioural changes.  

Parkinson’s clinical trials are one of the most challenging areas of research with a lack of therapeutics achieving desired disease modification. One of the major causes for treatment failing to progress to market is the placebo effect. 

The patients’ desire to retain independence and basic functions means that the placebo effect reduces symptoms even beyond experimental treatment. As a result, drugs fail to achieve the end-point as the observed improvements are primarily psychological and outweigh the efficacy of the drug. 

Gene therapy 

Over the last decade, Parkinson’s research has shifted towards cell and gene therapy (CGT). Current treatment is designed to replace the dopamine lost in the brain which, long-term, causes a multitude of side effects and does not modify the disease. 

Gene therapy is a broad term which covers methods that improve the genetic profile of an organism by “means of the correction of altered (mutated) genes or site-specific modifications that have therapeutic treatment as target”. 

The introduction of gene therapy for Parkinson’s has been split into possible targets classed as disease-modifying or non-disease modifying. Disease modifying strategies revolve around stopping PD-mediated cell death and/or regenerating lost neurons.

Previous Parkinson’s clinical trials of CGT have been based on viral vectors to deliver therapeutic transgenes to neurons within the basal ganglia. This delivery system remains a popular choice in the field and at the forefront of gene therapy. 

Bayey has initiated a gene therapy unit through Asklepios Biopharmaceutical which is currently recruiting patients for a phase 1b trial of a gene therapy using an adeno-associated viral (AAV) vector. This delivery system is designed to “deliver a gene for human glial cell line-derived neurotrophic factor (GDNF) into the neurons in the putamen”. GDNF is a naturally occurring protein in the body, previously shown to support the growth, survival, and maturation of dopaminergic neurons. This supports the rationale of delivering GDNF which could stimulate the growth of dopaminergic neurons and recover the loss of dopaminergic signalling which is essential for symptom recovery. 

Stem cell therapy 

BlueRock Therapeutics, a subsidiary of Bayer Pharmaceutical is utilising an alternative approach for Parkinson’s CGT.  According to a recent industry article,” in the BlueRock trial the first patient has been administered the first dose of pluripotent stem cell-derived dopaminergic neurons – MSK-DA01. The drug was delivered via surgical transplant procedures into the putamen area of the brain”. 

Pluripotent stem cells are cells that have the capacity to self-renew and develop into any cell of the adult body. By dividing and developing into the three primary germ cell layers of the early embryo, pluripotent stem cells can differentiate into any tissue type. 

Induced pluripotent stem cell (iPSC)-derived dopaminergic (DA) neurons have remained at the forefront of Parkinson’s CGT research. Midbrain dopaminergic neurons can be efficiently induced from human embryonic stem cells and iPSCs and, when grafted into the target brain region, can improve the impaired behaviour of rodent and non-human primate (PD) models. 

The success of preclinical studies supporting the efficacy of stem cell transplantation for dopaminergic restoration in PD models has played a huge part in accelerating clinical studies of CGT in humans.  

Potential CGT challenges – delivery system 

The delivery system of CGT varies across the pharmaceutical industry, but is usually divided into viral- and non-viral vectors. Traditional viral vector-based gene therapy is achieved by in vivo delivery of the “therapeutic gene into the patient by vectors based on retroviruses, adenoviruses (ADs) or adeno-associated viruses (AAV)”. 

The ideal delivery system for gene therapy in the central nervous system (CNS) will aim to follow a specific criteria: (1) minimally invasive delivery (2) target specific tissue (3) achieve long-life treatment following a single, low dose. One of the main obstacles for gene therapy in the CNS is the blood brain barrier (BBB). 

As a protective measure, the BBB prevents the diffusion of drugs (delivered intravenously) into the brain. This was a particular issue raised for AAV vectors, highlighted in a journal review. However, direct delivery into the CNS is one method of bypassing the BBB. This appears to be the direction for Bayer’s gene therapy, which will be administered directly into the putamen region.

This is known as an intraparenchymal injection, and a number of preclinical studies have demonstrated successful circumvention of the BBB. There are, of course, a number of challenges with this method of viral vector gene delivery highlighted in the aforementioned review:

• This method is highly invasive which may not be an appropriate method for clinically vulnerable patients who may find this distressing 
• Previous studies have demonstrated the distribution of AAV particles within the brain (of higher order animals) is restricted 
• Intracranial delivery is correlated with a lower probability of therapeutic efficacy in larger mammals 

For CGT, one of the greatest barriers is the immune system, which could attack the stem cells/genes delivered into the brain. For AskBio’s gene therapy, this is a potential risk. Humoral immune responses, derived from antibody-producing B cells, can develop against AAV. Humoral immunity refers to antibody-mediated immune responses. This is also a challenge for BlueRock Therapeutics stem cell therapy, in which the transplanted cells will be destroyed by the immune system as a protective mechanism against foreign bodies. 

However, the transition from preclinical to Bayer’s clinical trials infers that there is potential for success and safety for both cell and gene therapy. The phase one trial for Bayer’s stem cell therapy “primarily examines the safety and tolerability of cell transplantation a year after the procedure. Secondary goals include assessing evidence of cell survival following transplant and motor effects one and two years after surgery”. In other words, this will determine the efficacy of the delivery system with regards to symptom improvement and highlight any potential safety issues. 

These clinical trials represent a huge milestone in Parkinson’s research. For the first time, there is potential to develop therapies which could overcome the short-lived efficacy and extensive side effects of current Parkinson’s treatment. For a significantly unmet clinical need, this could offer a hope for a growing patient population. Furthermore, successful disease modification within these CGT trials could potentially lead to the development of CGT for other diseases of the CNS.

• thus specific gene expression silencing is obtained 

siRNAs have been described as the most promising type of RNA-based therapeutic oligonucleotide drug. The ability to inactivate RNA molecules in a sequence-specific reinforces potential for precision medicine in specific RNAi therapies. 

One of the main advantages of siRNAs over small molecules and monoclonal antibody drugs is the simpler execution of function. siRNAs work by complete Watson-Crick base pairing with mRNA, whereas small molecule and monoclonal antibody drugs need to recognise the complicated spatial conformation of certain proteins. 

As a result, there are many diseases, especially rare forms, that are not treatable by small molecule and monoclonal antibody drugs due to the complexity of the pathology in which a target molecule with high activity, affinity and specificity cannot be identified. 

Despite this significant advantage, there remain a number of barriers for siRNA to their targets. Firstly, siRNAs are unable to directly penetrate the cell membrane and can enter the cell only by endocytosis or pinocytosis. However, in order to implement the silencing effect, siRNA must penetrate the membrane and exit into the cytoplasm.

When siRNA enters the cytoplasmic space via endo/pinocytosis, there is also a risk it will be cleaved by cytoplasmic ribonucleases or its concentration can decrease due to the division of target cells. This is an obvious issue when it comes to optimising the bioavailability of the drug for two reasons: (1) concentration is affected by internal cellular mechanisms, and 2) is susceptible to degradation by enzymes. 

The high specificity of action which makes siRNA so desirable can also cause off-target effects. As a result this limits its use in high concentrations due to the induced toxicity. The most significant non-targeted effect of siRNA is unwanted activation of the system of innate immunity under the action of certain motifs in the siRNA sequence. 

Fortunately, research in this area has led to chemical modifications that may affect the properties of siRNA. 

One of the most saturated areas of interest is the use of bioconjugates, like with ASOs, which enables a more direct delivery system to target cells. A stable linker binding siRNA and biomolecules together prevents a decrease in the efficiency of RNAi associated with the inhibition of RISC assembly. Lipids and cholesterol were some of the first ligands suggested for conjugation with siRNAs as they can overcome the issue of the hydrophobic cell membrane barrier.

On-going research is determined to address some of the challenges with siRNAs and ASOs through chemical modifications to improve bioavailability and reduce toxicity. Despite these challenges, the efficacy of these drugs remains evident with an increasing number reaching the market. The end goal is optimise the delivery of these drugs to the point where they can be potentially developed for effective precision therapeutics.

Charlotte Di Salvo, Junior Medical Writer
Proventa International

The post Latest Advancements in Parkinson’s Treatment: CGT Shows Potential to Reverse Disease appeared first on Proventa International.

As of 23 June 2021, there are 14 variants of COVID-19 which are of concern or under investigation in the UK, according to the UK government. The unique profile of COVID-19 enables rapid mutation, producing variants which are more transmissible and deadly. Fortunately, the current vaccines against the virus remain effective, and on-going clinical trials investigate the efficacy of winter booster shots. 

For daily articles on the latest pharma trends and innovations, as well as interviews with leading experts and in-depth industry White Papers, subscribe to PharmaFeatures.com.

As a result of natural selection, viruses naturally mutate through errors in the viral genome. RNA viruses have much higher mutation rates in comparison with DNA viruses – mutations which do not interfere with the essential virus functions will persist in the viral population. 

The rapid spread and transmission of COVID-19 globally has provided the virus with the opportunity for the natural selection of rare but favourable mutations which have enabled its survival. Although most viral mutations are benign, many mutations, such as D614G on the spike (S) protein, strengthen viral survival capability. 

Coronavirus is a single-stranded RNA virus and contains one of the largest genomes for RNA viruses. Variants of COVID-19, a novel form of coronavirus known as SARS-CoV-2, arise from genetic mutations which alter the spike (S) protein on the virus.

Since early January 2020, hundreds of new mutations were found on different residue positions of SARS-CoV-2 S protein. The S-protein is the key to infection within humans, as it mediates the binding of the virus to host cells which promotes entry and the later release of viral particles. The S protein is the main antigen component in all structural proteins of SARS-CoV-2. Antigens lie on the surface of viruses which trigger an immune response. 

Mechanism of vaccines

Vaccines take advantage of the highly evolved human immune system, and essentially trick it into thinking it has detected a virus in order to promote the production of antibodies by B cells, specific to the antigen of interest. 

The antibody level remains high in the body for up to two weeks after infection, which allows memory B cells to store copies of these antigens specific to the virus. 

There are a number of COVID-19 vaccines that have been developed in the last year and include DNA- and RNA-based. RNA-based vaccines are derived from a molecule known as mRNA, which is the “intermediate step between the translation of protein-encoding DNA and the production of proteins”. 

RNA vaccines initiate an immune response by introducing mRNA into the cells which have been engineered to carry the genetic instructions to produce the S protein. Through protein synthesis, the S protein is created which stimulates an immune response in the host and copies are stored by memory cells for future immunity should the body encounter the virus.

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

This is a simplification of the mechanism for the mRNA COVID-19 vaccine.

In the development of the COVID-19 vaccine, it was important that the mRNA of the virus was modified. Unmodified viral mRNA is highly immunogenic and as a result can activate many pathogenic sensors which is a safety risk. In a 2021 study, it was emphasised that modified mRNA of the virus didn’t activate these sensors which was crucial to avoid excessive inflammation resulting in undesired side effects.

Vaccines against variants – the latest updates 

As of today 16 June 2021, the current vaccines available, including AstraZeneca and Pfizer, continue to show protection from the Delta variant (B.1.617.2; formerly the ‘Indian’ variant).

Real world data from AstraZeneca shows a 92% effectiveness against hospitalisations due to the Delta variant (after a second dose). The Pfizer-BioNTech vaccine has shown to be 88% effective against symptomatic disease from the B.1.617.2 variant two weeks after the second dose.

According to the government, however, both vaccines were only 33% effective against symptomatic disease from the Delta variant three weeks after the first dose, compared to around 50% effectiveness against the Alpha variant. With increasing numbers of people fully vaccinated, there is still a level of protection against the Delta variant for those with one dose thanks to herd immunity and the restrictions in place. 

Boosters

The world-first COVID-19 vaccine booster study has recently launched in the UK, investigating the safety and efficacy of booster shots for COVID-19 protection in winter. According to the government, “the trial will look at seven different COVID-19 vaccines as potential boosters, given at least 10 to 12 weeks after a second dose as part of the ongoing vaccination programme. One booster will be provided to each volunteer and could be a different brand to the one they were originally vaccinated with.

Vaccines being trialled include Oxford/AstraZeneca, Pfizer/BioNTech, Moderna, Novavax, Valneva, Janssen and Curevac, as well as a control group”.

The COVID-19 booster shot will work in a similar fashion to the seasonal flu booster. Like COVID-19, the influenza virus has a tendency to mutate rapidly. Seasonal flu shots protect against the “three or four influenza viruses that research suggests may be most common during the upcoming season”. 

The shots, which will be developed from the current vaccines, will ‘boost’ the immune system of those clinically vulnerable who naturally possess a weaker immune system. 

A virus requires a host in order to survive and mutate, therefore, booster shots against COVID-19 in the winter will hopefully, and theoretically, reduce the prevalence of the virus in the population, minimising its chance to mutate further. 

Charlotte Di Salvo, Lead Medical Writer
PharmaFeatures

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