For years, biologists believed the amino acid sequence of each protein determines its three-dimensional structure, which, in turn, determines its function. However, there is a large group of proteins and regions that lack a fixed or ordered 3D structure, yet they still exhibit essential biological activities—so-called intrinsically disordered proteins.

It turns out that these intrinsically disordered proteins may be the key to better overcoming diseases in neurodegeneration, diabetes, cardiovascular disease, amyloidosis, genetic diseases, and cancer. This peer-reviewed journal published in ACS Infectious Diseases reveals a landscape analysis of this emerging topic and identifies critical insights across therapeutic areas, from SARS-CoV2 to genetic diseases and cancers. The deep dive within discoveries around intrinsically disordered proteins and the opportunities ahead will enable faster progress for future therapies. Read the full publication here.

In the past decade, there has been a shift in research, clinical development, and commercial activity to utilize RNA for medicine. With the rapid success in the development of lipid–RNA nanoparticles for mRNA vaccines against COVID-19 and several approved drugs, RNA has catapulted to the forefront of drug research. This peer-reviewed publication in the ACS Journal of Medical Chemistry uses the CAS Content Collection to examine the multifaceted benefits of RNA in drug development. This includes an examination of the potential it serves as a new method of therapeutics, either as a drug or a target. Also studied is the current landscape of RNA research and trends in medicine. 

Carbon capture and the road to Net Zero

It is paradoxical that whilst carbon dioxide (CO₂) is essential for all plant life on Earth upon which all animal and human life depends, too much of this essential gas in the atmosphere is causing global warming, threatening the very survival of some populations.  

The problem of CO₂ emissions from increased burning of fossil fuels dates to the 18th century, with the start of the industrial revolution in certain countries. Today, scientists have estimated the rise in average global temperatures is expected to reach 1.5^oC by 2030–2052 (Figure 1). The volume of CO₂ emissions is exacerbated by industrialization, urbanization, and sharply increasing world populations (Figure 2). 

The 21st Conference of Parties on Climate Change (COP21) in 2015 adopted an ambitious target called the ‘race to zero’ by 2050. This momentous goal of eliminating net carbon emissions completely in under 30 years will require universal changes in global industrial processes and domestic energy practices. The better-known methods to achieve this goal involve various sustainable forms of power generation, such as wind and solar power; however, a less publicized but equally important approach is the capture of CO₂ at the source or directly from the atmosphere (carbon capture). The technologies involved are restricted by high costs and somewhat limited storage capacity so currently, only 0.1% of global CO₂ emissions are sequestered; this is predicted to rise to 19% by 2050. Research efforts in carbon capture technology have increased in recent years, but to date, only a few applications have been commercially deployed. With increasing public awareness and urgency around preventing or reducing climate change, the pressure is on to devise more efficient carbon capture technologies.

Carbon capture in the CAS Content Collection™

The CAS Content Collection™ is the largest human-curated collection of published scientific knowledge, suitable for quantitative analysis of global scientific publications against variables such as time, research area, formulation, application, and chemical composition. To assess the recent and ongoing research effort into carbon capture, an important new CAS Insight provides an overview of the latest trends. The Insight summarizes the results of an extensive recent analysis (∼18,500 documents published between 2000 and 2021) detailing terms related to carbon capture, including methods employed, storage or conversion, which were used in combination with terms related to atmospheric CO₂ or its environmental effect.

Key research trends and carbon capture methods

The literature analysis revealed that since 2008, there was a rapid increase in all carbon capture and storage publications, which slowed after the mid-2010s, but more recently, has increased again. This could reflect prevailing economic conditions and perceived urgency but also appeared to be linked to oil prices. When oil prices are low, carbon capture seems too expensive, so sequestration efforts and storage tend to be limited. The analysis retrieved a small number (10%) of patents related to carbon capture, indicating low commercial interest in this technology; recently, however, the numbers have shown an encouraging steep growth.
The various approaches to capture carbon fall into four categories: material science, biological, chemical, and geological.

Material science approaches

Material science approaches, including systems for carbon capture from flue gases, are summarized in Figure 3 and Table 1. Among these, post-combustion capture is the most widely used being suitable for retrofitting to flues on existing power plants but employs much energy and is therefore expensive to run. An emerging method, direct air capture, in which CO₂ is captured directly from the air, could have a wide application, but this process is made more difficult by the low concentration of atmospheric CO₂ and has a high cost.

Table 1. Material science methods: comparison of CO₂ capture processes

The key methods of carbon capture from flue gases are summarized in Table 2. These encompass chemical absorption with an alkaline solution and physical absorption using non-corrosive solvents such as methanol or Selexol. Additional approaches include adsorption into porous solid adsorbents, which is well studied, and membrane filtration, which is an emerging technology but is not yet widely used due to low CO₂ separation efficiency. 

Table 2. Material science methods: comparison of CO₂ capture methods

Biological approaches

Biological approaches to carbon capture largely take advantage of photosynthesis which accounts for the largest influx of CO₂ on Earth. Various plant materials such as wood or algae are converted to biofuels (biomass) for combustion, creating carbon-neutral and sustainable processes. Enzyme-based technologies have potential as alternatives to biosystems. A key example is 1,5-bisphosphate carboxylase/oxygenase (RubisCO) – a highly abundant and researched enzyme. Its CO₂ capture, however, is naturally slow, but ongoing work aims to increase RubisCO activity to create industrially viable processes.

Chemical approaches

There are also multiple chemical methods of carbon capture, such as catalytic processes involving reduction with hydrogen, which have been widely deployed on multi-ton scales. Other well-used methods include electrochemical processes in which protons and a catalyst are used to reduce CO₂. Photochemical, photothermal, and photoelectrochemical processes using clean energy are an interesting prospect, but as of yet, they are limited by efficient transfer of light energy to a substrate. Plasma-based processes also have potential but need high energy and require further development for use in carbon capture.

Geological approaches

Geological methods of carbon capture are a key solution to the long-term storage of CO₂ away from the atmosphere. Captured CO₂ can be compressed, transported, and injected into deep porous geologic formations or saline aquifers. This process has the capacity to store gigatons of CO₂, but the selection of suitable sites is critical.

CAS literature analysis on carbon capture

The CAS literature analysis revealed a low publication rate on CO₂ capture prior to 2007, but then rose to a peak during the early 2010s and then stabilized (Figure 4). There were fewer publications on pre-combustion and oxy-fuel combustion, most likely due to the economic difficulty in adapting current facilities, but these have increased more recently. Patent filing appeared to increase in 2012 and then stabilize, indicating continued commercial interest.

The CAS analysis also showed that publications on various chemical methods of CO₂ conversion increased rapidly over the last six years compared with years before that (Figure 5). Among these, methanation, plasma-mediated processes, and reverse water-gas shift methods showed the greatest interest.

Publication numbers indicate a rapid rise in interest in biological CO₂ fixation, but patent filing has been constant, reflecting a few limited technologies ready for commercialization (Figure 6). Publications on Bioenergy with Carbon Capture and Storage (BECCS), however, showed strong interest.

Publications on the geological storage of CO₂ increased steadily and peaked in 2013 but declined thereafter (Figure 7). Search terms including ‘aquifer’, ‘saline’, ‘brine’, ‘shale’, and ‘clathrate’ found more publications than others in recent years reflecting more interest in these types of storage. 

Turning the dream into reality 

The CAS literature analysis of 18,500 publications indicates substantial, rapidly increasing interest in many of the different approaches to CO₂ sequestration. At present, no method predominates; there are few that have been put into widespread use, but the analysis indicates considerable research effort into harnessing existing technologies and developing new ones. The numbers of patent filings were smaller than research articles but showed commercial interest in some technologies. The more recent results are likely to reflect increased public awareness of global warming and the realization that actions to counter it are imperative. The apparent correlation between research activity and economic conditions and the price of oil may diminish as urgency increases. The publication trends determined by CAS suggest that the pace of research and technology deployment is likely to continue at a rate only dreamed about in 2000 and are now driven by the realities of global warming becoming more apparent. 

RAS proteins: an elusive target? 

Around one in every five human cancers have at least one form of RAS mutation (K-RAS, H-RAS, and N-RAS), making RAS the most frequently mutated gene family in human cancers. RAS proteins, located on the cell plasma membrane, act as molecular switches sending signals for cell growth. However, mutations in RAS proteins may cause them to be constantly active and send growth signals uncontrollably, which leads to abnormal cell proliferation and cancer formation.  

Despite their prolificacy, there is a distinct lack of therapies that target these RAS proteins. RAS inhibitors have been investigated in cancer treatment for more than three decades, yet RAS proteins have come to be known as “undruggable” due to their elusive inhibitory potential – that is, until recently. Earlier this year the FDA approved sotorasib (developed by Amgen and commercially known as Lumakras™) for the treatment of lung cancer – the first RAS inhibitor to be approved as a therapy.  

The approval of sotorasib is a significant step forward in RAS inhibition, and research and development efforts in discovering further RAS inhibitors has intensified. Here, we explore how the gap is closing on RAS proteins – what were once considered elusive, undruggable targets are now emerging as a promising cancer treatment.

K-RAS: the most common mutation in RAS genes

The RAS genes code for proteins, which exist in four isoforms: K-RAS4A, K-RAS4B, N-RAS, and H-RAS. Mutated RAS isoforms, codon, and amino acid substitution vary by tissue and cancer type, but the most common are mutations in the K-RAS isoform – found in approximately 22% of RAS-mutated cancers. Eighty percent (80%) of K-RAS mutations occur at amino acid position 12, from glycine to other residues, including cysteine (G12C, 14%), aspartic acid, (G12D, 36%), and valine (G12V, 23%) (Figure 1).¹

Discovery of RAS proteins as candidates for cancer treatment 

RAS inhibitors can be identified by using X-ray crystallography. Using this method, RAS protein structures can be examined to identify potential binding pockets for small molecules to occupy within human cancer cells. This type of approach — structure-based drug design — makes it possible to discover hundreds of chemical substances that can potentially bind within specific pockets. The potential RAS inhibitors usually consist of a scaffold structure that is slightly modified with a variety of functional groups to enhance activity, selectivity, and decrease toxicity. This results in lead compounds that can be further analyzed, enhanced, and tested with the hope of evaluating them in human cancer clinical trials.

To gain a deeper understanding of the current RAS inhibitor landscape, we’ve reviewed the patents and publications related to RAS inhibitors in the CAS Content Collection™. The analysis revealed 26,958 chemical substances with therapeutic or pharmacological roles in the direct RAS inhibitory space. The number of chemical substances and patents in this space have increased per year, underpinning how research interest and efforts towards RAS inhibitor discovery are gaining pace (Figure 2). 

The recent FDA approval of Amgen’s sotorasib led to a significant uptick in research efforts towards RAS inhibitor discovery. Sotorasib is a covalent inhibitor of KRAS G12C via the Switch-II pocket. It was the first KRAS inhibitor to be approved for use in human cancer treatment and to treat KRAS G12C mutated non-small cell lung cancer (NSCLC) (Figure 3).²

Four additional KRAS-G12C inhibitors are currently in clinical trials, including MRTX849, which is based on a similar core structure to sotorasib (Figure 4)². Different functional groups resulted in different binding mechanisms to key elements of the Switch II pocket. MRTX849 earned FDA breakthrough therapy designation for KRAS G12C positive NSCLC in June 2021. 

The journey continues: expanding the breadth of direct RAS inhibitor targets 

As more molecules binding RAS are discovered, more surfaces on RAS isoforms and RAS proteins are being identified as potential small molecule targets. 

As mutated RAS isoforms, codon, and amino acid substitution vary by tissue and cancer type, varying approaches are needed from the current G12C inhibitors to increase the range of cancer therapies. Future opportunities include expanding the type of amino acids that can be targeted by inhibitors, such as G12D and G12V, which may broaden the types of cancer we are able to treat. 

The gateway is now open for RAS inhibition and by gaining a better understanding of RAS oncoprotein structure and binding pocket configuration for small-molecule targets, novel RAS inhibitors can be developed and enhanced for optimal activity in RAS mutated cancers. 

Read our white paper to find out more about the continuing journey in uncovering RAS targets, including a more detailed overview of the current RAS inhibitor landscape of chemical structures and future opportunities.

References

1.    H. Chen et al., Small-molecule inhibitors directly targeting KRAS as anticancer therapeutics. J. Med. Chem. 63 (2020) 11404–14424. doi: 10.1021/acs.jmedchem.0c01312.

2.    L. Goebel et al., KRASG12C inhibitors in clinical trials: a short historical perspective. RSC. Med. Chem. 11 (2020) 760. doi: 10.1039/d0md00096e.

While some vaccines can take almost 15 years to be approved, the rate at which COVID-19 vaccines have been developed (1 year) is truly remarkable. The collaboration across disciplines, continents, and companies was unheard of and several emerging technologies emerged as key players.  From mRNA to lipid nanoparticles to cross protection of vaccines – this vaccine rush has re-shaped the landscape ahead. 

Since the World Health Organization declared COVID-19 a pandemic, researchers have learned a tremendous amount about SARS-CoV-2, the new coronavirus that causes this disease. However, despite extensive effort and investment, effective therapeutic treatments for COVID-19 patients have been elusive. Though multiple vaccine candidates have already entered clinical trials globally, even if they prove safe and effective, many months or even years will be required to manufacture and distribute the vaccine and inoculate the global population. Thus, there remains an urgent need to identify effective antiviral treatments that can mitigate the virus’ impact on the many more who will become ill before the pandemic is brought under control.

Scientists have been exploring various ways to accelerate the drug development process to meet this urgent need, including using computational approaches to identify drugs already approved for other indications that may be effective in treating COVID-19. To aid that effort, a group of scientists and technologists at CAS sought to identify possible drug candidates for treating COVID-19 with machine learning models for priority protein targets of SARS-CoV-2 using a Quantitative Structure-Activity Relationship (QSAR) methodology. This work, which successfully identified a number of drugs now beginning to show clinical efficacy, including Lopinavir and Telmisartan, was recently published in ACS Omega.

Something old, something new 

Given the substantial time and cost needed to bring a new drug to market, repurposing existing small-molecule drugs is an attractive alternative, especially when the need is so urgent. In addition to getting treatments to market faster, this strategy offers a number of advantages over the traditional drug development process, including lowering the risk of late-stage failure due to negative side effects. 

Drug repurposing is not a new concept. However, its application to date has been mostly opportunistic rather than systematic. In some of the most successful examples of drug repurposing so far, such as Viagra and Minoxidil, new indications arose when patients reported unexpected side effects. Recently, more systematic approaches to drug repurposing have been introduced including computational methods such as signature matching, molecular docking, genetic association, pathway mapping and retrospective clinical analysis. It is hoped that a computational approach will allow researchers to reliably connect existing small-molecule therapeutics to newly identified drug targets, maximizing the therapeutic value of existing portfolios.

Closing in on a target

Coronaviruses are a large family of viruses long known to cause mild to moderate upper-respiratory illnesses in humans and many different animal species. Though it is rare for animal-specific coronaviruses to infect and spread in humans, to date three coronaviruses have proven able to make that jump: SARS-CoV-1, MERS-CoV and the new SARS-CoV-2. All three are beta-coronaviruses believed to have originated in bats. Given the similarities between these viruses and their progress to human contagion, previous SARS and MERS research provides a good starting point when seeking drugable targets for SARS-CoV-2. Among all the proteins in SARS-CoV-2, the 3-chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp) are two ideal protein targets for QSAR modeling, in part due to significant similarities they share with proteins identified in SARS-CoV and MERS-CoV as well as other known coronaviruses.

3CLpro is a protease that is required for the coronavirus to cleave the polyprotein peptides into individual functional non-structural proteins (NSPs). When comparing amino acid sequences and protein structures, 3CLpro was found to be highly conserved between SARS-CoV-2 and other human coronaviruses. It shows a 96% sequence identity overlap with SARS-CoV-1, 87% with MERS-CoV, and 90% with Human-CoV. Therefore, the 3CLpro inhibitors identified in previous coronavirus-related research are promising inhibitors for SARS-CoV-2 3CLpro, and the associated structure-activity relationship (SAR) data are valuable for training machine learning models searching for new inhibitors of SARS-CoV-2 3CLpro. 

RdRp is the major enzyme utilized by RNA viruses to replicate viral genomes in host cells. Structural study and sequence analysis of SARS-CoV-2 RdRp revealed that this enzyme is very similar to the structure of SARS-CoV-1 RdRp and contains several key amino acid residues that are conserved in most viral RdRps, including HCV. Fortunately, various viral RdRps have been widely studied as inhibitors of RNA viruses, especially in HCV-related research. Therefore, existing RdRp inhibitors for the RNA viruses, such as HCV, may provide valuable insights for drug development for SARS-CoV-2 RdRp inhibition.

Prioritizing existing therapeutics with machine learning 

Machine learning models have increasingly been used to facilitate drug discovery in recent years. Specifically, QSAR is often one of the first steps in the modern drug discovery process. Simply put, QSARs are mathematical models approximating rather complicated biological or physicochemical properties of chemicals based on quantitative measures of their molecular structures. These predictive mathematical models are used for screening large databases of chemical structures to prioritize potential drug candidates that are most likely to be active against identified targets. This approach assumes that the activity of a chemical substance is directly related to its structure, and thus, molecules with similar structural features will exhibit similar physical properties and/or biological effects.

In this study, my colleagues and I closely collaborated to build highly predictive QSAR models for 3CLpro and RdRp protein targets. The team, which included computational scientists and chemists, curated more than 1,000 inhibitors with structure-bioactivity data as training molecules for the models. We collected data from the most current SARS-CoV-2 bioassay studies, as well as existing studies with SARS-CoV-1, MERS-CoV and other related viruses in the CAS content collection. Using these data, we applied a variety of machine learning algorithms to build several dozen QSAR models – selecting from among these the strongest performing models – one targeting 3CLpro and one targeting RdRp.

Read the full journal article QSAR machine learning models and their applications for identifying viral 3CLpro- and RdRp-targeting compounds as potential therapeutics for COVID-19 and related viral infections to see all the models tested and which potential candidates rose to the top.

We used the two resulting QSAR models to screen a large pool of potential drug candidates including 1,087 FDA-approved drugs, nearly 50,000 substances from the CAS COVID-19 Antiviral Candidate Compounds Dataset and ~113,000 substances with pharmacological activity identified or a therapeutic role indexed by CAS in SARS-, MERS- and COVID-19-related documents published since 2003. By modeling protease inhibitor activity as a function of substance structure, we identified some of the most promising candidates among substances predicted to be active inhibitors of coronavirus 3CLpro and RdRp. Additionally, a number of the substances that our models predict will inhibit 3CLpro or RdRp in SARS-CoV-2 also have previously identified therapeutic activity against other diseases that have emerged as risk factors for more severe COVID-19 infections. For example, a candidate COVID-19 antiviral that also has known activity against heart disease, such as diltiazem hydrochloride (Cardizem), could potentially provide a dual benefit, in certain cases.

The models were validated to have high area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity and accuracy (Figure 1). In the time since this research was completed, some molecules predicted to have high activity by these models have now been validated by published experimental bioassay studies and clinical trials, providing further positive indication of their predictive ability.

Getting ahead of the next pandemic

While this study was focused on identifying potential therapeutic compounds for use in the current COVID-19 crisis, it is likely there will be additional pandemics of viral origin in the years to come. Thus, it is urgent that preparation for future outbreaks begins now with continued investment and focus on antiviral agent research. Because different types of viruses can cause epidemics (e.g., coronavirus, influenza viruses, Ebola viruses, retroviruses) and human safety and efficacy testing for each new drug or indication still takes significant time, broad-spectrum antiviral agents and vaccines would be of greatest value. 

The ongoing development of computer-based drug discovery methods, such as the machine learning procedures described here, molecular docking and virtual screening, will be of central importance. The ongoing increase in computer processing power and continued development of docking and structure prediction algorithms and protein crystal structure determination techniques will facilitate progress. Additionally, the use of high-throughput screening, omics technologies and the repurposing of already-developed drugs will continue and increase in importance. However, these new technology-driven methods won’t replace human laboratory research, but will instead complement it through increased efficiency. We hope this effort, which combined human data curation and machine learning models to successfully identify potential small-molecule drug candidates for COVID-19, highlights the value of synergy between humans and machines in drug discovery, while contributing to on-going antiviral research efforts for COVID-19 and beyond.

As part of the global scientific community, CAS is committed to leveraging all of our assets and capabilities to support the fight against COVID-19. Explore our additional open-access CAS COVID-19 resources including scientific insights, open access datasets and special reports.

Lipid nanoparticles were propelled into the global spotlight for their role in two approved COVID mRNA vaccines. However, lipid nanoparticles aren't unique, in fact their development started decades ago as liposomes and as critical drug delivery vehicles. Only now, has more and more opportunities for advancement and applications emerged.

While much of the recent spotlight with mRNA has been focused on the COVID-19 vaccines, the use of RNA in therapeutics has the potential to revolutionize medicine for generations ahead.  This landscape view of the emerging field of RNA therapeutics highlights emerging trends in targets, chemical modifications, and new delivery systems that increase stability of RNA.

Artificial intelligence (AI) and machine learning have been growing exponentially over the last decade, but how has the field of Chemistry evolved with this emerging trend?  This latest CAS white paper -  "Artificial Intelligence in Chemistry: Current Landscape and Future Opportunities " -  explores the landscape of AI and chemistry using our own technologies to map the publication and patent trends.

We have uncovered the areas of chemistry that are leading the field with AI and those with a great potential yet to be unlocked by the adoption of AI technology.

When pandemics strike, drug re-purposing becomes critical for faster development of therapies. However, assembling all of the critical information and connections around new proteins, new viruses, targets, pathways, and clinical information can be challenging. CAS leverages their unique connections across the world's science for novel knowledge graphs that identify top clinical candidates to re-purpose for COVID-19 therapies.

This complimentary CAS insights report details the CAS Biomedical Knowledge Graph and demonstrates how we have used it to provide solutions for a real-world problem – drug discovery in COVID-19.