What's new

What's New

13 | Jun | 2024


The use of Artificial Intelligence (AI) in drug discovery has spawned a fast-growing biopharmaceutical segment that is estimated to be $1.5B in 2024 and expected to grow to $14B in 20321. There are innumerable reports and publications on how AI enables drug discovery in specific areas. One segment is the identification of drug targets – AI algorithms can analyze available data and identify patterns and trends much faster than humans but large unbiased datasets are needed to train the algorithm to be effective. For example, if high throughput small molecule screening data is available for a disease target, then that dataset can be used to train the AI platform to identify a manageable number of more potent and specific compounds for that target. Essentially, AI can help prioritize quality over quantity at the hit screening stage. Other areas where AI can have a large impact is around study planning and process workflows. One large area is the optimization of clinical trials including the identification of relevant patient populations and predicting patient response1. Another critical area, especially for advanced modalities such as cell and gene therapies, is supply chain optimization around demand forecasts and inventory management1. It is not uncommon for drug developers to face challenges around regulatory filings that have complex procedures1 and AI can enable the automation of regulatory documentation especially for complex modalities.

There have been several successful reported milestones of AI-enabled drug discovery and at least 2 reports of AI-designed drugs entering clinical trials. The first AI-designed drug to enter clinical trials was developed by Exscientia in collaboration with the Japanese pharma company Sumitomo Dainippon, which was a serotonin 5-HT1a receptor agonist designed to treat obsessive compulsive disorder2. Though the drug failed in Phase I as it did not meet success criteria2, the program is a landmark achievement in AI-enabled drug discovery. Another promising example is INS018-055, a novel small molecule inhibitor of the TNIK enzyme that is implicated in fibrosis3. It is important to note that the Generative AI used by InSilico Medicine to identify this candidate also identified the novel disease target. The novel small molecule has received Orphan Drug Designation from the FDA and is in Phase II clinical trials for the treatment of Idiopathic Pulmonary Fibrosis (IPF), a rare incurable disease4. Additionally, AI is being deployed to predict structures for all known proteins and this project is called the AlphaFold Protein Structure database5, and it is expected that AI will be deployed in several areas of basic research to identify new drug targets and signaling mechanisms.

The preclinical success of AI-enabled therapies has fueled significant adoption across the drug discovery community – a recent publication from a consulting group reported that since 2015, 75 drugs have entered the clinic and about 90% of those therapies are still in the clinic6. The therapies in the clinic are a mix of AI-discovered small molecules, antibodies and vaccines as well as repurposed drugs6, which is a positive indication that AI-enabled drug discovery can be used across multiple modalities. However, one interesting caveat is that the disease target was known for most of the therapies identified using AI7, which suggests that fundamental new target identification studies using conventional methods are required to support AI-enabled drug design. Nevertheless, the growing success of AI in the identification of novel and next-gen therapies suggests that the time and cost associated with preclinical drug discovery is likely to decrease, so new therapies may be a lot closer to patients.









04 | Jun | 2024


Since the passage of the FDA Modernization Act 2.0 in December 2022, there has been a rapid acceleration in the development of NAMs or New Approach Methodologies. NAMs cover a broad range of technologies including in silico drug design, cell-based in vitro assays and next-generation rodent models. The development of NAMs has been accelerated by key macrotrends such as favorable legislation, increased societal pressures and significant scientific and technological improvements. Regulatory agencies in the US and Europe are continuing to make significant investments in the development of NAMs. These include increased funding1and the passage of legislation to incentivize drug developers to use in vitro assays to evaluate the safety and toxicity of new therapies2. Social media platforms are playing a critical role in spreading awareness about animal use in preclinical drug development and this has spurred biotech and pharma companies to be more thoughtful on using animals in preclinical efficacy and toxicology studies. However, one challenge with social media is that misinformation and inaccurate data are easily spread, so it is essential for the scientific community to continue to share educational information with the general public. While there are many reasons to use non-animal models and platforms for evaluating new therapies, it is important to make sure that these methods are equivalent or superior to animal models, and that the datasets are large enough to identify statistically significant effects of therapeutics. While some studies such as systemic PK/PD and ADME are currently conducted in in vivo models and would be very difficult to replace with in vitro models, there are a few groups working on very complex multi-organ microphysiological systems (MPS) linked with precision microfluidics3 that could replace in vivo models in the future.

In vitro models cover a broad range of platforms thar range from simple 2D cultured cell lines complex multi-organ MPS. In vitro models have several advantages over animal models including being all human, which eliminates species variability issues and a controlled testing environment, which reduces data variability. However, in vitro platforms have limitations including the inability to reproduce systemic effects or highly complex biologic processes and limited timelines that may not allow for longitudinal studies. Since each platform has unique pros and cons, it is essential for drug developers to elucidate the context of use and identify the experiment objective. Once the objective is clear, then the appropriate model or platform can be identified and established. At first glance, it seems that the more complex in vitro model systems (MPS) would be the optimal choice for most studies, as they are highly translational, but MPS systems have high costs, long timelines and complex workflows.

Typically, in vitro models work well when there are a small or moderate number of clearly defined endpoints. One such example is screening candidates identified in high throughput screens (HTS) that can reverse or halt loss of neuronal function in neurodegenerative diseases. A platform of choice would be using human induced pluripotent stem cells that are differentiated into various neuron types combined with an electrophysiological readout measured using multi-electrode arrays (MEA). Another example of context of use is genotoxicity assessment where the ability of new therapies to damage DNA is assessed using well-established endpoints such as the micronucleus and COMET assays. Traditionally, genotoxicity assays use animal models and different tissues including liver and blood cells are tested for DNA damage. The in vivo genotoxicity assays are in the process of being replaced with a battery of cell-based assays with established endpoints like the micronucleus and COMET assays. A controlled in vitro system is also suitable for the development of new endpoint assays like whole genome sequencing to assess global mutational changes and DNA damage. These are a couple of examples of the context of use for specific in vitro assays and as the drug development community continues to develop NAMs, we can expect to see more clearly defined contexts of use to answer specific scientific questions.





16 | May | 2024


There are about 500 kinases identified in the human genome that range from minimally studied to very well characterized drug targets1. Kinases are broadly classified based on the substrates that include carbohydrates, lipids and proteins. Protein kinases are the largest group and can be further classified based on the amino acid residues that are phosphorylated (serine/threonine or tyrosine). Since kinases are involved in critical signaling cascades including cell proliferation, and metabolism, they are involved in disease development across multiple areas including cancer, autoimmune disease, neuronal disease, inflammation, metabolic disorders etc. About 25-33% of drug development programs target kinases2 and these programs have resulted in the successful development and approval of 79 drugs targeting kinases3. The first kinase inhibitor (imatinib) was approved in 2001 for the treatment of chronic myelogenous leukemia (CML) that carry the BCR-Abl gene rearrangement4. A majority of the approved kinase inhibitors are used to treat different tumor types, especially hematological cancers that can be quickly diagnosed and are amenable to easy drug administration. However, as more potent inhibitors were developed for use in humans, a pattern emerged where the kinase inhibitor therapies seemed to be most efficacious in early-stage disease and did not work very well in late stage or metastatic disease4. Kinase inhibitor drug development typically has multiple generations where the first-generation therapy is developed and approved for a targeted patient population with a specific mutation. The next-generation of therapies are rapidly developed to be more potent and potentially target broader patient populations for a given tumor type or even target other tumor types.

One of the most important characteristics of kinase inhibitors are their promiscuity which can be a positive or negative attribute. Kinases tend to be conserved around the catalytic domain so it is very common for a given inhibitor to target multiple kinases. The positive aspect of this is that a single inhibitor can be used to treat multiple cancer types with different mutations or rearrangements. One example of the positive polypharmacology is crizotinib that was developed as a Met kinase inhibitor for non-small cell lung cancer (NSCLC) but was found to have activity against the ROS/ALK gene fusion that is a NSCLC disease driver5. However, one of the big issues with kinase drug development are off-targets caused by an inhibitor binding to multiple kinases that can result in unacceptable toxicity.

One specific application of kinase drug development is the inhibition of angiogenesis. The scientific hypothesis is that inhibiting the development of vasculature in a solid tumor, nutrients and oxygen supply will be cut off resulting in tumor cell death. The VEGF receptor family has been extensively targeted and one specific therapy, bevacizumab, has had significant clinical success4. Nevertheless, kinase inhibitors targeting angiogenesis have had challenges due to cross-reactivity with other kinases.

In recent years, the success of immune checkpoint inhibitors such as pembrolizumab have occupied the limelight for novel cancer therapies but the development of kinase inhibitors has been progressing in the background. The continued development of kinase inhibitors was highlighted by the approval of a first in class Akt inhibitor capivasertib in November 20236. Capivasertib has been approved for use along with fulvestrant, an estrogen receptor antagonist, for hormone receptor positive/HER2-negative breast cancers with known PI3K/Akt/PTEN mutations6. Since the drug is approved for a specific breast cancer population, the FDA also approved a companion diagnostic test (FoundationOne®CDx assay)6. Clinical trial data showed that the combination of capivasertib and fulvestrant reduce disease progression by 50% compared to fulvestrant alone7. The approval of capivasertib is a landmark achievement for Akt related drug development programs as other therapies such as ipatasertib failed to achieve clinical success in castration-resistant prostate cancer and triple-negative breast cancer7.









23 | Apr | 2024


Neurodegenerative diseases encompass several disorders that are typically associated with the death of specific neuron types resulting in loss of motor, cognitive and other abilities, and most of these diseases are ultimately fatal. It is estimated that there are more than 600 neurodegenerative disorders that impact 50 million Americans each year1. Despite the growing unmet clinical needs, there has been limited progress in developing new therapies for neurodegenerative diseases. Drug discovery and development rely heavily on preclinical in vitro and in vivo models to study disease biology, identify new drug targets and test therapies. While different models have distinct advantages and drawbacks, the selection of a given model should be carefully done to answer specific biological questions. For example, cell-based models derived from primary disease state neurons or induced pluripotent stem cells (iPSC) lines are widely used to study disease pathology and identify mechanism of action for new drug targets2. Additionally, cell-based models are well suited to screen small molecules or biomolecules to identify candidate therapies to test in animal models. Traditionally, cell-based models consisted of simple 2D cell cultures but increasingly there is a shift towards using more complex 3D cell models that are cultured on a scaffold to mimic the tissue environment2. While the in vitro models are useful in the early stages of drug discovery, the gold standard to evaluate therapeutic efficacy and safety are animal models.

Several rodent models of neurodegenerative disease are used in preclinical discovery programs for new therapies. These models include transgenic models where specific disease-causing mutations are introduced or chemically induced models to induce neurological damage3. Despite the large number of publications and funding, mouse models of neurodegenerative disease have been shown to have limited translation to human patients as therapies that were shown to be efficacious in mouse models had very limited effect in humans3. One of the key reasons for this is that the rodent CNS is very different from the human CNS in terms of anatomy, physiology and neuronal complexity3. Additionally, rodents have a short lifespan so it is difficult to model age-related neurodegenerative disorders such as Alzheimer’s disease and Parkinson’s disease. Nonhuman primates (NHPs) are better suited to model neurodegenerative diseases as the NHP CNS is well suited to evaluate changes in cognition, brain function and motor skills that are hallmarks of neurodegenerative diseases. Aged NHPs have been reported to develop age-related issues such as neuron loss, plaque formation and cognitive deficits similar to humans4. However, it is expensive and complicated to maintain an NHP colony for long time periods to study the natural development of neurodegenerative diseases. Therefore, the development of induced disease models is of interest. For example, the injection of beta-amyloid containing brain tissues into NHP brains were shown to induce plaque formation, neuroinflammation and other neuronal issues4 associated with Alzheimer’s disease. However, NHP models of Alzheimer’s disease have not been widely adopted likely due to ethical and cost issues. Animal models of Parkinson’s disease can be induced by the injection of specific chemicals such as MPTP or 6-hydroxydopamine. These compounds can be injected directly into the brain or systemically into the vasculature, skin or muscle. NHP models injected with MPTP recapitulated the motor skill deficits associated with Parkinson’s disease4. More recently, intracerebral injections of gene therapy vectors encoding mutant alpha-synuclein or Lewy body extracts in rhesus macaques and cynomolgus NHPs induced neuronal cell loss and increased expression of alpha-synuclein but these physiologic changes did not translate to motor skill changes4.

While Alzheimer’s disease and Parkinson’s disease are challenging to model since the exact genetic drivers of disease development are not fully known. In contrast, the disease driver for Huntington’s disease has been identified as an expansion in the number of CAG repeats in the huntingtin gene. Researchers have introduced the mutant huntingtin gene using lentiviral vectors into specific brain regions of cynomolgus macaques and demonstrated the development of Huntington’s disease symptoms4. An attempt to develop a transgenic NHP model was reported but the animals with the mutant huntingtin gene had very short lifespans4. To overcome this disease-related challenge, researchers have developed iPS cell lines from transgenic NHP models of Huntington’s disease to study disease biology and reported the development of an NHP iPSC-derived astrocyte model for Huntington’s disease5.

In summary, while NHP models are highly translational and recapitulate several hallmarks of neurodegenerative diseases, the complexity of developing and maintaining these models pose significant challenges.







08 | Apr | 2024


Nonhuman primates (NHPs) are highly translational models in drug development and are widely used in preclinical efficacy and safety studies1. Due to genotype similarities with humans, in vitro NHP stem cell lines have started gaining traction within the drug discovery community as translational models to evaluate preclinical efficacy and safety. Initially, pluripotent stem cells were initially developed from mice followed by humans. Human embryonic stem cells (ESCs) were first described in 19982 but faced significant challenges due to ethical issues and societal pressures. A major breakthrough was reported in 2006 when Yamanaka and colleagues described the development of human induced pluripotent stem cells (iPSCs) that were reprogrammed using specific transcription factors3. Since the first report, human iPSC lines have been widely developed and used for basic research, in vitro assays for drug development and are the foundation of cell therapies and regenerative medicines. Methods and protocols to develop novel human stem cell lines are widely available but, interestingly, the development and use of NHP stem cell lines have somewhat lagged behind. One reason could be that reprogramming NHP cells to iPSCs has lower efficiency and the protocols are more complex compared to human cell reprogramming1. For example, NHP iPSC reprogramming require feeder cells and xenogeneic serum and depending on the NHP species, can take several weeks1. Despite technical challenges, NHP stem cell lines have been successfully developed and are being used in multiple applications including regenerative drug development and cell therapies as well as primate developmental biology studies.

One of the key applications of NHP pluripotent stem cells is preclinical testing of cell therapies. During preclinical development, the dosage, route of administration, implantation efficiency, short- and long-term efficacy and host rejection need to be evaluated. NHP iPSCs are well suited to test cell therapies in NHP models prior to clinical trials4. NHP models are well suited for longitudinal studies to evaluate host rejection and graft vs host disease as they have an extended life span and large bodies that is amenable to imaging, surgical and sampling methods that are used in the clinic4. NHP pluripotent stem cells have been reported to be similar to human stem cells so the preclinical data on cell therapies is translatable to human patients. A key application of NHP pluripotent stem cells is the evaluation of immunogenicity responses to cell therapies. The easiest approach to avoid immune reactions is to focus on autologous cell therapies, which have limited scalability and require complex supply chain logistics among other challenges. Allogeneic cell therapies are scalable and easier to manufacture but typically induce host rejection both locally and systemically. Therefore, in order to safely develop allogeneic cell therapies, it is necessary to analyze immune risk and tissue damage caused by the host immune system. The NHP immune system is very similar to human so testing NHP pluripotent stem cell derived therapies in an NHP model is a good model to evaluate immunogenicity4.

NHP pluripotent stem cells have applications in regenerative medicine. For example, researchers in Göttingen Germany reported a new method to reprogram NHP skin fibroblasts to iPS cells that were successfully differentiated into cardiac muscle cells1. The NHP iPSC derived cardiomyocytes had self-organizing capacity and were shown to generate beats via microelectrode array (MEA) analysis1. Another growing area of interest is regenerative therapies for neurodegenerative diseases such as Parkinson’s disease (loss of dopaminergic neurons) and Huntington’s disease (loss of basal ganglia neurons). Over the past decade, a few groups have developed autologous transplantation NHP models for Parkinson’s disease4 and have continued to improve the transplantation process. A recent publication demonstrated the most advanced model where dopamine neural progenitor cells were transplanted in NHP models of Parkinson’s disease and were shown to reduce disease symptoms significantly with lower immune risk over a 2-year period5. As expected, autologous transplantation showed longer engraftment with low immune risk compared to allogeneic transplantation.

In summary, it is clear that NHP pluripotent stem cells have disease specific applications to evaluate advanced modalities such as cell therapies. It is likely that the next generation of NHP iPSCs will have improved reprogramming and differentiation efficiencies and NHP stem cells will have an important role in the translational drug development toolkit.







29 | Feb | 2024


Therapeutic antibodies have traditionally been developed via one of two method – phage display and transgenic mouse models. Both approaches have been shown to be successful as several antibody therapies are available commercially. Another method that is gaining interest is B cell screening but as of now, no approved antibody has been developed using this method. The first monoclonal antibody therapy was approved in 198611, and as of June 2022, 162 antibody-based therapies have been approved for commercial use2. It is estimated that at least 23 marketing applications for antibody-based therapies will be submitted by the end of 2023 and this number includes 5 bispecifics and 2 ADCs (antibody-drug conjugates)3. Despite the clinical and commercial success of antibody-based therapies, there are challenges with the current methods that include high costs, long timelines and limited success with targets that have low immunogenicity. Additionally, it is difficult to target functional epitopes and it is important to note that strong binding may not be an indicator of function.

Antibody drug developers are increasingly turning to artificial intelligence (AI) based methods to improve process efficiency and quality of antibody candidates. Several companies are developing AI-powered workflows for antibody discovery. One such company is LabGenius based in the UK that combines automation, machine learning and disease-relevant readouts in an algorithm that identifies antibodies that can differentiate between a normal and disease state based on known readouts4. The process supports the identification of unexpected candidates as there is minimal human intervention and due to the automation and speed of the machine learning process, it takes about 6 weeks from start to finish4. Another example is a recent report from Xtalpi, a company based in China5 who used humanized mouse-generated antibodies as the input data to build AI models. The team combined a large antibody sequence dataset with an AI model that can predict pairing of antigen epitopes and antibody sequences thus reducing the time and costs associated with screening and identifying antibody candidates in mouse model5. Simply put, the AI driven workflow can accurately simulate a humanized mouse model for antibody development in a fraction of time and expense.

Recently, generative AI methods are being used to power antibody discovery. Generative AI is the method used to create language-based algorithms such as ChatGPT and uses patterns and structures from the input training data to create new data. In the antibody discovery space, generative AI can be used to design endless antibody combinations from large data sets that have been built from antibody campaigns6. The unique aspect of using generative AI is the zero shot concept where the algorithm can design new structures that it has not been trained on. This means that the zero shot method can be used to generate antibodies for new targets without requiring training data for that specific target. Given the power of this approach, it is not surprising that companies have adopted the generative AI approach for identifying novel therapeutic candidates including small molecule inhibitors and therapeutic antibodies7. Absci, an antibody discovery company, recently reported the identification of antibodies targeting human EGFR and Her2 using a combination approach of generative to AI to create close to 3 million design per week and high throughput screening to validate antibody candidates that bind to the target antigen8. This combination process eliminated several steps in the antibody development process including optimization of in silico candidates and time-consuming lead optimization studies.

As generative AI becomes integrated into antibody discovery workflows, it increases the probability of identifying novel antibody candidates for diseases that had complex pathophysiology or were deemed “undruggable”. If this potential is realized, then generative AI could completely transform the early-stage screening and identification of therapeutic candidates across multiple modalities.










13 | Feb | 2024


Preclinical toxicology studies are required for every therapeutic development program as these studies answer fundamental questions on the local and systemic effects of the test drug on the patient. Typically, tox studies are performed in small and large animal models and use defined endpoints. The guidance issued by the FDA has clearly stated the minimum requirements for preclinical toxicology studies include PK/PD profiling, acute toxicity studies in two species (rats and dogs are the most commonly used) and short-term toxicity studies to evaluate continued and potentially delayed onset adverse effects1. Traditional toxicology studies have been more observational and record the ADME characteristics, biodistribution and PK/PD profiling along with optimal dose ranges that have acceptable off target effects. However, there is an increasing shift towards a more active investigational toxicology approach that can be either prospective or retrospective2. Prospective investigative toxicology, as the name suggests, is performed during the discovery stage to quickly identify promising drug assets that have low toxicity and can move forward into efficacy evaluation. This approach supports the concept of “fail early and fail fast” so that assets with unacceptably high levels of toxicity are removed early from the development pipeline, thus saving significant time and downstream costs. These prospective studies are typically performed in translational in vitro models that range from simple 2D cell culture models and 3D organoids to highly complex microphysiological systems (MPS) such as organ-chips3. The retrospective approach is focused on understanding the mechanism of action of adverse effects identified in in vivo animal models or clinical trial patients. These studies can use multiomics-based approaches to review global changes in gene and protein expression profiles in response to drug exposure combined with ADME, histopathology and PK/PD data. The retrospective analysis is very useful to design next-generation therapies that can bypass the signaling triggers that cause off-target effects.

Prospective investigative toxicology studies are recently gaining traction due to the interest in responsible animal use and regulatory willingness to accept data generated in in vitro and ex vivo models. The FDA Modernization Act in the US and the activities by European medical agencies to promote animal-free testing has accelerated the development of complex in vitro model systems to predict toxic effects. It is important to note that in vitro model development has moved at different paces depending on the organ. For example, lung MPS model development was very rapid in response to the COVID-19 pandemic, while liver and kidney MPS development are moving at a slower pace in part due to tissue complexity. The availability of high-quality input materials impacts the pace of development – for example, researchers are dependent on hepatocellular carcinoma (HepG2) cells or primary hepatocytes to test therapies for drug induced liver injury (DILI), which is a critical tox readout. These models are not fully representative of the in vivo state and, in the case of primary hepatocytes, supply and quality continue to be issues. The development of reliable, high-quality iPSC-derived hepatocytes has been a challenge but as reprogramming technologies continue to improve, it is likely that this challenge will be solved. Another example is the development of translational complex kidney models. Simple 2D overexpression models have been used for several years to study drug-drug interactions (DDI) but the recapitulation of kidney glomeruli in vitro is a complex issue. Nephrons, the functional units of the kidney, consists of over 20 cell types that are arranged in a complex structure4 but MPS platforms typically use 2 cell types – epithelial cells and endothelial cells. Micro-physiological systems (MPS) for kidney cell culture were first reported in 2013 with the development of a kidney chip5 that showed expression of uptake and efflux transporters, resulting in accurate and reproducible responses to known transporter inhibitors such as cimetidine. Bioprinting is another technology that is being investigated to develop a 3D model of the kidney for the use in investigative toxicology studies4.

It is clear that the development of complex in vitro models for investigative toxicology is on an accelerated pace. As the development of input materials and culture systems continue to improve and evolve, the combination of biology and engineering will result in human in vitro systems that recapitulate the in vivo state to better predict off target effects.







02 | Feb | 2024


Imaging methodologies are critical in the diagnosis and prognostic monitoring of solid tumors in humans. Methods such as CT (computed tomography), MRI and PET are widely used in humans and these methods have continued to improve in terms of resolution, sensitivity and data analysis. More recently, the use of AI enablement was reported to improve detection of different solid tumors including skin, breast and head and neck1. Additionally, the combination of different imaging modalities such as MRI and PET have been shown to increase accuracy of tumor detection1. The use of imaging methods to noninvasively detect and monitor tumors in preclinical oncology animal models is becoming more widespread especially due to the translational value of the protocols, tracers and data analysis methods2. Similar to humans, multi-modal preclinical imaging can be used to obtain data on various tumor characteristics including size, morphology, metabolic activity, vasculature and inflammation2.

Preclinical imaging methods can be segmented into the following types: MRI, CT, ultrasound, photoacoustic (PAT) imaging, PET, SPECT and optical imaging (fluorescent and bioluminescent imaging)2,3. MRI is considered the gold standard of imaging modalities and has been shown to have the best tissue resolution that can be enhanced with specific tracers2. Additionally, there are various subtypes of MRI that are tailored to measuring specific characteristics – for example, tissue oxygen levels can be measured via functional tissue oxygen-level dependent MRI that could be used to monitor response to radiotherapies2. The use of contrast agents such as gadolinium chelate allow the visualization of changes in blood vessel architecture in tumors and there are ongoing studies to use gadolinium-based agents to identify cell surface receptors in tumor cells2. Certain imaging methods are more suited for specific tissues types – for example, CT imaging is the optimal method to identify lung lesions due to excellent contrast between air and tissues2. Clinically, ultrasound imaging is the method of choice to detect pancreatic cancers in both human and animal models. Preclinically, ultrasound is also sued used to guide orthotopic model development by helping researchers inject cells in the correct tissue space2 and can be combined with PAT imaging to provide physiological data on the tumor. The basic principle of PAT imaging uses short laser pulses to irradiate tumor tissues leading to heat induced tissue expansion that creates acoustic waves4. The acoustic waves can be measured using ultrasound4.

PET imaging is used to monitor physiological changes in metabolic activity, vasculature etc. and uses radiolabeled tracers such as 18F-fludeoxyglucose (FDG) to monitor glucose uptake in tumors. Since tumors are more metabolically active than surrounding tissues, 18F-FDG PET imaging is a useful method to monitor tumor size and evaluate changes in tumor metabolism after therapeutic intervention5. While 18F-FDG is the most well-known tracer, PET imaging can be performed using multiple radiopharmaceutical tracers and some of the tracers can also be used for SPECT imaging which uses a gamma camera instead of a positron emission scanner. One of the key advantages of using PET and SPECT imaging is that radiolabeled tracers can be used to monitor specific reception expression levels or physiological markers2. For example, 18F-fluorothymidine can be used to monitor DNA synthesis and cell proliferation in tumors. Given the huge focus in immune-oncology, “immuno-PET” has emerged as a specific imaging method where antibodies to select receptor targets or T-cell targeting molecules can be labeled with radiopharmaceutical tracers to monitor the response to specific checkpoint inhibitor therapies2,5. One such reported tracer is a 64Cu-labeled Axl antibody that was used to monitor the efficacy of an hsp90 inhibitor (17-AAG) to downregulate Axl regulation in triple negative breast cancer6.

In vivo optical imaging methods such as fluorescent and bioluminescent require the insertion of a fluorescent tag or a luciferase enzyme into tumor cells or the therapeutic modality2. The tags can be inserted into microbes, viruses, antibodies, peptides etc. so noninvasive luminescent imaging is an easy way to track tumor cells or therapeutic modalities in an animal model. While several fluorescent proteins are used in preclinical studies, one challenge is autofluorescence in specific tissues that can obscure or interfere with the fluorescent signal2. Bioluminescent imaging using luciferase reporters has gained significant traction in preclinical in vivo studies and there is active research to engineer more sensitive luciferase enzymes that have more catalytic activity and improved emission signals7









16 | Jan | 2024


Lipid nanoparticles (LNPs) are vesicle composed of lipids that are used to deliver a wide range of therapeutic modalities including nucleic acids (DNA, mRNA, siRNA), antibiotics and small molecules (such as doxorubicin)1. The most well-known application of LNP drug delivery are the mRNA COVID-19 vaccines developed by Pfizer/BioNTech and Moderna. Fundamentally, LNPs are spherical vesicles composed of ionizable lipids whose charge changes in response to pH2. LNPs have neutral charge at physiological pH, which facilitates entry into cells but have positive charge at acidic pH to promote complex formation with negatively charged nucleic acids. LNPs are internalized into cells via endocytosis and release the payload in the cytoplasm upon exposure to low pH2. LNPs can take various forms including liposomes, nano-emulsions, solid lipid nanoparticles, nanostructured lipid carriers, and lipid polymer hybrid nanoparticles1. Liposomes are best known for delivering chemotherapies such as doxorubicin and paclitaxel for cancer treatment and lipid polymer hybrid particles have also been used to deliver docetaxel for treatment of various cancers1. The nanostructured lipid carriers and solid nanoparticles have been used to deliver nucleic acid therapies. Apart from therapies, LNPs are gaining interest in cosmeceuticals which is an unregulated space that primarily consists of skin and hair care products3. LNPs have desirable properties for topical applications as they adhere well to the skin and easily disperse across the tissues. However, since this space is not overseen by regulatory agencies like the FDA or EMA3, it is important for manufacturers to manufacture and test the LNPs to ensure high quality standards.

Various types of LNPs have been used to deliver different therapeutics, antibiotics and sedatives since the 1990s. A majority of the therapies use liposomes4, and the first LNP based siRNA (Patisiran) therapy was approved in 2018 for the treatment of hereditary transthyretin amyloidosis2. The mRNA based COVID-19 vaccines that were approved in 2021 also used LNPs to deliver mRNA targeting the spike protein of the SARS-CoV2 virus. However, it is important to note that LNPs have pros and cons. One of the key advantages of LNPs is the low toxicity rate since the lipids are biocompatible and do not trigger significant toxicity. Structurally, LNPs are very stable and are amenable to tissue targeting. Depending on the target tissue or organ, LNPs can be directly administered via nebulization to the lung or direct injection into the eye5. LNPs have natural tropism to the liver so they are well-suited to target hepatic diseases and this property is being used to engineer LNPs to deliver payloads to the liver at high efficiency. Additionally, LNPs can be targeted to immune cells such as T-cells via specific antibodies such as anti-CD45. Currently, there is active research to develop next-generation LNPs that have specific tissue targeting properties. LNPs also have certain disadvantages and the major challenge is the low drug loading and delivery efficiency. While this is not a major issue for vaccines, it is of concern to deliver drugs in sufficient quantity to exert a therapeutic effect. LNPs also have short blood circulation time and are susceptible to removal by macrophages causing a low number of LNPs reaching the target tissues. While LNPs are considered to be the most clinically advanced nonviral gene delivery method, the current status of the field restricts LNP use to specific applications but the fields of use are likely to grow with improved next-gen LNPs.







04 | Dec | 2023


Antibody drug conjugates (ADCs) are targeted therapies that consist of a monoclonal antibody (mAb) linked to a chemotherapeutic via a linker. The mAb binds to a target receptor antigen on tumor cells and the ADC complex is internalized into tumor cells resulting in the release of a chemotherapeutic drug that kills tumor cells. The main advantages of developing an ADC are low off target effects and an expanded therapeutic index of the chemotherapy resulting in more effective tumor killing. The concept of using a mAb to deliver a cytotoxic payload to tumor cells is not new but early attempts to develop clinically effective ADCs were unsuccessful due to a few reasons such as poor linkage resulting in separation of the payload, off-target toxicity due to nonspecific antibody binding, immune responses resulting in rapid clearance and low residency time etc1. Additionally, ADCs can be developed against specific membrane bound receptors that have an antigenic extracellular domain that does not get released or shed into the extracellular environment or vasculature1. ADC targets are typically overexpressed in tumor cells compared to normal cells so the continued discovery of differentially expressed biomarkers will help the development of novel ADCs. Currently, most ADCs target receptors or ion channels that are difficult antigen targets, but improvements in antibody discovery methods have helped improve the quality of therapeutics antibodies used in ADCs.

Another critical element of developing an ADC is the linker technology. Using a weak or incorrect linker could result in early release of the payload causing systemic toxicity or cause aggregation of the ADC complexes2. Currently, there are 2 classes of linkers – cleavable and non-cleavable3. Cleavable linkers are primarily cleaved in one of 3 ways – protease, reduced pH or in the presence of reduced glutathione2,3. Enzyme mediated linker cleavage is commonly used and about two-thirds of approved ADCs employ this appriach2. Non-cleavable linkers typically fall in 2 categories – thioether and maleimidocaproyl and are generally considered to be superior to cleavable linkers3. ADCs with non-cleavable linkers are dependent on cellular lysosomal degradation so release of the chemotherapeutic agent only occurs in tumor cells. Therefore, ADCs with non-cleavable linkers have more stability in the vasculature and have a larger therapeutic index and there is active research to develop new and improved linkers.

ADC payloads have historically been available chemotherapies that inhibit cell proliferation, but recently, novel payloads have been used. One example is Enhertu whose payload is a topoisomerase I inhibitor that can inhibit DNA replication in tumor cells4. Another example is Lumoxiti, an ADC targeting hairy cell leukemia whose payload is a Pseudomonas exotoxin A4. Lumoxiti has been recently discontinued due to low market uptake but is an example of creative payload design.

Currently, there are 11 approved ADCs in the US and over 150 ADCs in clinical trials5. 2 ADCs (Mylotarg™ and Blenrep™ were discontinued due to failure to meet endpoints in post-marketing approval clinical trials but Mylotarg was re-approved at a lower dose5. Due to the clinical success of ADCs, biopharma companies are investing significantly in the space leading to the renaissance of ADCs6. Several large pharma companies such as Pfizer and Astra Zeneca have announced large acquisition or ADC asset deals signaling that pharma companies are interested in developing and commercializing ADCs6. There are a couple of major reasons why pharma is interested in ADC assets. The ADC technology platforms have improved significantly and the current third generation of ADCs demonstrate high target specificity while evading the immune system. Additionally, newer ADCs with superior linker technology can carry more payload. One example of a superior ADC is Enhertu that was approved in December 2019 for HER2-positive metastatic breast cancer6 that has a drug antibody ratio or DAR of 8. The Phase III clinical trial data for Enhertu showed a stunning 72% reduction in disease progression6 and was a major clinical success. From an economic point of view, ADCs are difficult therapies to develop biosimilars due to the multiple components, so ADC developers have a longer window to generate revenue and have more pricing power6.

Given the technological advancements in monoclonal antibody development, linker chemistry and payloads along with a track record of clinical success and high barrier to entry for biosimilars, there is no doubt that ADCs are experiencing a true renaissance and this is positive news for many cancer patients with limited therapeutic options.