15 | Nov | 2023

Chinese

The drug development and manufacturing industry in China has historically focused on generic drugs and Chinese CROs have heavily depended on drug development business from Western countries. However, these trends have changed significantly in the past several years, and it is becoming clear that the biopharma sector in China is growing rapidly with a focus on technology innovation and first-in-class drugs. This change started in 2015, when China’s regulatory agency, the National Medical Products Administration (NMPA), started a series of reforms and changes to accelerate in-country drug development and expand clinical trials ¹. One of the key reforms was the decoupling of drug development and production where the drug developer does not have to be the drug manufacturer². This decoupling allows companies to focus on innovative drug discovery without the need to divert resources to process development, scale up manufacturing, quality control and lot release of the drug product. Secondly, the Center for Drug Evaluation (CDE) issued guidelines for conducting clinical trials across multiple therapeutic areas including oncology and rare diseases², thus encouraging more in-country trials of novel therapies. Additionally, the funding environment to support Chinese biopharma companies has grown significantly in the past several years. There has been a rapid increase of available capital through VC firms and more relaxed regulations for companies to go public including the formation of the STAR Board³ in Shanghai.

Notably, in 2023 there have been several licensing deals where Chinese biotech companies have developed and licensed drug assets to big pharma and conversely, have in-licensed several drug assets⁴. This bidirectional licensing activities suggests that Chinese biopharma companies have gained traction on the world stage as developers of high-quality therapeutic assets. Licensees include top pharma companies such as GSK, Takeda and AstraZeneca and the total value of the top 10 licensing deals range from $2 billion to $700 million⁴. However, the deals where Chinese biotech are the licensees tend to have a lower total value and range across multiple disease areas including oncology, infectious disease and liver disease⁴. Unsurprisingly, as the number of biotechs developing licensable assets increases, China based CROs are also growing likely to accommodate the increased outsourcing needs.

The 2022 top global CROs list includes 3 Chinese companies – Wuxi AppTec, Pharmaron, and AsymChem⁵. The revenue growth projections of China based CROs continues to be strong with an anticipated market growth 13% in 2021 to 19% in 2024⁵. While China based CROs have always been known to have deep expertise in chemistry and small molecule drug development, in the past few years there has been a rapid growth in advanced modalities including monoclonal antibody-based therapies. Importantly, the CROs are no longer dependent on business from North America and Europe since Chinese biopharma companies are outsourcing to in-country CROs. This trend likely started during the COVID-19 pandemic but has continued to hold strong as Chinese CROs have developed impressive end-to-end capabilities across the drug discovery continuum. It is estimated that about 40% of WuXi Biologics, a leading Chinese CRO, client base is in country⁶. Another key development has been that China joined the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use in 2017, allowing the use of data from clinical trials run in China to be used in filings to multiple regulatory agencies⁶. Since this development, the clinical CROs based in China can compete with global clinical CROs as they can generate usable data at a more economical price tag compared to several North American and European CROs⁶.

It is clear that China continues to be a competitive player in the preclinical and clinical drug development sector and is expected to grow at a significant pace. This growth is fueled by available capital, supportive government regulations, lucrative licensing deals and an aggressive strategy from Chinese CROs to support domestic biopharma companies while penetrating Western markets.

References:

¹https://globalforum.diaglobal.org/issue/june-2021/chinas-new-era-of-reform-transforming-regulatory-professionals/

²https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326289/

³https://www.pharmexec.com/view/china-invests-in-building-biotech

⁴https://baipharm.chemlinked.com/news/2022s-top-10-cross-border-licensing-deals-involving-chinese-biopharma-companies

⁵https://baipharm.chemlinked.com/news/china-contract-research-organization-cro-2022-review-and-outlook

⁶https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/chinese-drug-contract-research-companies-see-surge-in-domestic-demand-63832298

06 | Nov | 2023

Chinese

The drug development pipeline is increasingly moving towards complex therapies such as monoclonal antibodies and their derivatives, cell and gene therapies and nucleic acid (DNA and RNA) therapies. The critical requirement to support clinical trials and successful commercialization of advanced therapies is high quality consistent manufacturing that is primarily outsourced by pharma to CDMOs (contract development and manufacturing organization). The rapid growth in the complex therapies pipeline has fueled the need for manufacturing capacity at CDMOs that is supported by experienced talent and establishing quality systems. Not surprisingly, the pharmaceutical CDMO market is experiencing high growth with a current marker size of $95B in 2022 that is expected to grow to over $170B in a decade with an estimated 6.2% CAGR¹. While estimated market sizes do vary across reports, most reports suggest that the CDMO market is expected to double in the next decade or so.

Historically, drug developers have been primarily based in North America and Europe (NA and EU) who have partnered with CDMOs in the same geographies². This has largely been due to the interest in building and maintaining a close bond between drug developers and CDMOs. CDMOs in North America and Europe have had access to premier talent pools and manufacturing expertise across multiple advanced modalities. It is estimated that about 37% of CDMOs have comprehensive end-to-end portfolios², which, helps foster strategic partnerships with drug developers. However, a survey in 2014 revealed an interesting trend that drug developers did not consider geographic location to be a key consideration for selecting a CDMO³ suggesting that the CDMO globalization trend was only a matter of time.

The dominant position of North America and European CDMOs is being challenged by an increasing trend towards globalization especially in Asia. There are a few key reasons for this globalization trend – capacity, supply chain and expertise. Drug developers are increasingly concerned about a “capacity crunch” at NA and EU CDMOs that can delay manufacturing and negatively impact the race to be first to market⁴. This has led to drug developers towards CDMOs in China and India that have available capacity. Additionally, the COVID-19 pandemic had a significant impact on supply chain availabilities and costs of raw materials and consumables resulting in large price increases and manufacturing delays⁵. The rush towards manufacturing COVID-19 vaccines and antiviral therapies has helped equip several Asia based CDMOs with the infrastructure and knowhow to manufacture advanced biologics and complex therapies at a high quality.

Specifically, China is experiencing extremely high growth in the CDMO sector at an estimated 32% CAGR⁶. One of the major reasons that triggered this growth is a legislative change in 2015 where Chinese drug developers did not have to build in-house manufacturing facilities and could use CDMOs to manufacture drugs for approval⁶. This allowed the CDMO sector to grow rapidly and develop advanced capabilities to manufacture biologics therapies. India is another growing CDMO geography and this is largely being fueled by low costs and the availability of skilled labor⁶. Other emerging geographies for CDMO outsourcing include Latin America and Africa but these seem to be initially focused on vaccine production.

An interesting trend that is being observed is the shift of Asian CDMOs to North America and Europe. Several savvy CDMOs that were originally based in Asia (primarily China) are recognizing the need to have a presence in drug development hubs in the US and other countries. This shift is helping increase the profiles of CDMOs that originated in Asia but are now global organizations with end-to-end capabilities to support drug development and manufacturing globally. As this trend increases, it is clear that the CDMO world will move towards a virtual global space where physical distances are irrelevant and drug developers will have a broad range of global CDMO partners to accelerate advanced therapies to market.

References:

¹https://www.globenewswire.com/en/news-release/2023/06/07/2683991/0/en/Pharmaceutical-CDMO-Market-Size-Will-Expand-to-USD-172-02-BN-by-2032.html

²https://www.strategyand.pwc.com/de/en/industries/health/2022-global-cdmo-study.html

³https://www.pharmtech.com/view/biomanufacturing-outsourcing-globalization-continues

⁴https://www.bioprocessonline.com/doc/top-trends-in-biomanufacturing-for-0001

⁵https://www.bioprocessonline.com/doc/s-bioprocessing-year-in-review-key-takeaways-0001

⁶https://www.cphi-online.com/emerging-regional-markets-show-promise-as-cdmo-news114072.html

13 | Oct | 2023

Chinese

The drug development process has several critical milestones. One of the milestones is pharmacokinetics (PK) studies, which is the study of how a given drug interacts with the body. PK studies typically evaluate the ADME or absorption, distribution, metabolism and excretion of a new therapy. Crossing the BBB poses a significant challenge for several therapies that target brain diseases including neurodegenerative diseases and brain tumors. PK studies performed in the CNS are especially important to ascertain how much of a given drug crosses the blood brain barrier (BBB)¹ to have a therapeutic effect on brain tissues. If the drug cannot cross the BBB efficiently, then it is likely to have limited therapeutic efficacy at an acceptable dosage. It is important to understand the barriers between blood, CSF and the extracellular fluid (ECF) to appreciate the complexities of drug transport into the brain. The BBB separates blood flow from brain tissue and is a tight barrier with no gap junctions or pores, while the BCSFB (blood CSF barrier) is more porous and supports vesicular pinocytosis for the transport of biomolecules including drugs². Since CSF freely interacts with blood and is in fact produced from blood plasma, drugs that are administered systemically are detected in CSF². CSF can deliver drugs to specific areas of the brain³. The most common administration methods are lumbar puncture and ICV or intracerebroventricular injection³. Measurement of the available drug concentration and metabolites in CSF after administration are typical readouts to assess ADME characteristics.

Typically, PK studies are performed in primate models that have similar brain anatomy and physiology as humans. PK studies are complex and require analysis at multiple time points to map out the effect of the drug on the body, so it is essential to use minimally invasive methods for repeated sampling of biofluids. There are a couple of different methods to access biofluids in the CNS. One approach is CSF sampling through lumbar puncture or through the cisterna magna, and another approach is through microdialysis where a probe is placed in the tissue of interest to facilitate sampling⁴. Both approaches have their uses and limitations. CSF sampling typically uses lumbar puncture for repeated sampling of the spinal CSF, while microdialysis samples ECF around the tissue of interest⁴. Secondly, microdialysis is more widely performed in rodent models with limited use in nonhuman primates, while CSF sampling is well established in nonhuman primates. Microdialysis methods are useful to analyze the immediate environment surrounding a brain tumor or brain region of interest while CSF sampling provides a more global picture of free or unbound drug concentrations. Typically, samples from a microdialysis probe are used to evaluate changes in secreted proteins and neurotransmitters and locally expressed biomarkers. On the other hand, CSF sampling can be used to evaluate global biomarker changes as the sampling is typically done at a site that is distal to the tissue of interest. Interestingly, some reports have shown no significant differences in drug PK characteristics between CSF samples and ECF samples acquired through microdialysis⁵. Therefore, it is important to select the appropriate sampling method depending on the experiment objective and animal model of choice. In summary, sampling and analyzing the CSF are essential to evaluate both direct drug delivery and drug pharmacokinetics in the CNS.

References:

¹https://pubmed.ncbi.nlm.nih.gov/15381336/

²https://link.springer.com/article/10.1007/s10928-013-9301-9

³https://www.sciencedirect.com/science/article/pii/S0169409X21000685

⁴https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388052/

⁵https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151035/

02 | Oct | 2023

Chinese

CNS (central nervous system) tumors are primarily located in the brain with some tumors in the spinal cord. CNS tumors can be of various types and are typically named for the cells that are involved – for example, astrocytomas are tumors growing in astrocytes¹. Globally, over 308,000 people are estimated to be diagnosed with a CNS tumor and about 25,000 adults in the US are expected to be diagnosed per year². Additionally, over 4,000 children are diagnosed with a brain tumor and most cases have a poor prognosis². Given the limited therapeutic options for CNS tumors, early and accurate diagnosis of tumors is critical to improve prognosis and survival rates.

Accessing the brain tissue directly is complicated and invasive but the cerebrospinal fluid (CSF) is a viable alternative to assess cancer biomarkers. CSF is a body fluid that circulates over the brain and down the spinal cord. Since it comes into contact with brain tissue and tumors, secreted biomolecules or cancer cells diffuse into the CSF and can be detected using established analytical or cell based assays³. CSF sampling is typically done using a lumbar puncture where a needle is inserted into the spinal cord between the vertebrae. However, cancer cells and secreted biomarkers are not typically found in abundance in the CSF and the analysis may not always be reliable. Therefore, there is a need for more sensitive assays to identify low abundance biomarkers or cancer cells³.

Current assays to identify cancer cells include cytology analysis where CSF samples are analyzed under a microscope, and flow cytometry analysis to identify cancer cell surface markers. A few recent studies have demonstrated that circulating tumor cells (CTCs) in the CSF can be detected used the FDA approved CellSearch® system⁴. The CellSearch system was originally approved to detect CTCs in breast, colorectal and prostate cancer⁵, but it has also been successfully used to identify CTCs in breast cancer related brain metastases⁶. It is likely that as more studies with larger patient cohorts are performed, CTC detection in the CSF may become a standard diagnostic tool to identify brain metastases as well as CNS tumors.

CSF samples are rich in different biomarkers that are typically proteins or microRNAs. Changes in protein composition in CSF from normal vs cancer patients can be measured using ELISA or IHC based assays as well as proteomic analysis or mass spectrometry. A study in 2006 used a mass spectrometry-based method to identify elevated levels of carbonic anhydrase as a marker for gliomas⁶. Several studies have compared normal and malignant patient samples and have identified levels of specific markers³. While the results of these studies show promise, it will be important to thoroughly validate tumor type specific biomarkers to meet regulatory requirements for diagnostic testing. MicroRNAs (miRs) are short noncoding RNA fragments that bind to the 3’ end of mRNA and inhibit protein translation. There has been an explosion of interest in developing miR based therapies and several miRs have been identified as high potential drugs for specific tumor types. However, as of now, no miR based therapies have been approved by the FDA but the interest in the biopharma industry continues to grow. Identifying miRs in CSF samples is of high interest as diagnostic biomarkers especially since panels of miRs can be used to diagnose specific CNS tumor types. An example of this panel type approach was reported in 2012 where 7 miRs were used to accurately identify glioblastoma and metastatic brain cancer⁷.

References:

¹https://www.mayoclinic.org/diseases-conditions/brain-tumor/symptoms-causes/syc-20350084

²https://www.cancer.net/cancer-types/brain-tumor/statistics#

³https://jcmtjournal.com/article/view/1321

⁴https://academic.oup.com/clinchem/article/68/10/1311/6661459

⁵https://www.cellsearchctc.com/

⁶https://pubmed.ncbi.nlm.nih.gov/17078017/

⁶https://pubmed.ncbi.nlm.nih.gov/22492962/

22 | Sep | 2023

Chinese

Genotoxins are chemicals or drugs or any entities that cause damage to chromosomes, DNA or RNA. The damage can result in mutations, single or double stranded DNA breaks and impaired transcription and translation. If the damage occurs in somatic cells, the consequences can include the development of tumors, cell death and inflammation but if the damage occurs in germ cells, it can cause heritable diseases, reproductive issues and birth defects. Drugs with genotoxic potential cause damage that may or may not be repaired by cellular mechanisms so if the repair mechanisms are not able to adequately repair the damage, mutations are generated that may have disease causing potential.

Due to the significant potential impact of genotoxic damage, it is critical to test new therapies for genotoxic stress potential. Since the endpoints of genotoxic testing are defined, several relatively simple bacterial and mammalian cell models are available¹ One of the earliest genotoxic tests was the bacterial Ames assay which assesses genotoxic potential by measuring mutations in specific strains of Salmonella bacteria that carry a mutation in the gene required to synthesize the amino acid histidine. The bacteria are cultured in media containing histidine and then exposed to the candidate drugs. The mutagenic potential of drugs is evaluated by determining if they cause reverse mutations and allow the bacteria to metabolize histidine in the culture and the number of bacterial colonies is a gauge of high, medium or low mutagenic potential².

Currently, two assays are popularly used to assess genotoxic stress – the Comet assay and the Micronucleus assay. The Comet assay uses single-cell gel electrophoresis assay to assess genotoxicity. The assay principle measures single- or double-stranded DNA breaks caused by drugs as cleaved DNA fragments migrate out of the cell when current is applied (ie. electrophoresis) while the undamaged DNA remains in the cell and forms the head of the comet. The denatured undamaged and cleaved DNA are stained with a DNA intercalating dye and visualized using fluorescence. While the Comet assay is simple and rapid and can be run on almost any eukaryotic cell, it does not shed any light on the mechanism of genotoxicity. The micronucleus test is also widely used to assess genotoxicity as micronuclei are essentially extra-nuclear bodies that include damaged chromosome fragments that result from chromosomal aberrations or genotoxic stress of specific drugs³. The chromosomal fragments from the micronuclei are not included in the nucleus after mitosis or meiosis so the genotoxic potential of drugs can be determined by counting the number of micronuclei. In many cases, the Comet assay and Micronucleus assay are both performed to assess the potential of drugs to cause DNA damage as well as chromosomal aberrations⁴. An interesting study from 2013 compared the Comet assay and Micronucleus for sensitivity and found that the Comet assay required higher doses of the test drugs and is less sensitive⁴. Nevertheless, both assay types provide valuable data on genotoxic stress. Research into the underlying mechanisms of genotoxicity is limited but some work has been done on drugs such as dacarbazine that is a chemotherapeutic approved to treat melanoma and Hodgkin’s lymphoma⁵. Dacarbazine is known to cause DNA methylation that impact transcription and translation.

At this time, the field is focused on using these assays to determine if specific chemicals, drugs or environmental toxins can cause DNA damage using simple endpoints but it is likely that more complex assays using next-generation sequencing will be broadly adopted to assess genome-wide genotoxic stress and understand mechanisms and hotspots for DNA damage⁶.

References:

¹https://www.sciencedirect.com/topics/medicine-and-dentistry/genotoxicity-assay#

²https://www.news-medical.net/life-sciences/What-is-Genotoxicity-Testing.aspx

³https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3708156/

⁴https://pubmed.ncbi.nlm.nih.gov/23863314/

⁵https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/genotoxicity

⁶https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003768/

30 | Aug | 2023

Chinese

Animal models have been the cornerstone of cancer drug development for decades and different types of tumor mouse models have been used extensively to study cancer biology and evaluate single and combination therapies. However, mouse models of cancer have also been widely acknowledged to have limited translational value and in many cases, do not accurately recapitulate tumor biology. This is especially true in the space of immuno-oncology where there are fundamental differences between the mouse and human immune systems. It is important to note that both simple and complex mouse models have a role in oncology drug development and the selection of the model is dependent on the scientific question that is being answered. For example, mice bearing subcutaneous tumors are useful for screening multiple drug assets for efficacy using simple endpoints such as tumor killing¹. Once promising assets are identified, more complex models are needed to understand the drug mechanism of action and off target effects.

There are several types of more complex mouse models that can be broadly segmented as transplanted models, carcinogen induced models and genetically modified models. In the past several years, there has been an increased focus on transplanting patient tumors into mouse models. Patient derived Xenografts or PDX models have become the mainstay of oncology drug development primarily due to the availability of patient tumors via biopsy and surgical excisions. The patient tumors can be implanted into animals that have compromised immune systems so that the mouse model does not reject the human tumor – while this model is useful to study tumor growth and development in an in vivo setting, it is not useful to evaluate therapies that target immune cells such as checkpoint inhibitors. Several research model providers have developed humanized mice where components of the human immune system are introduced into immune-compromised mice such as the NSG or NCG models. Human PBMCs (peripheral blood mononuclear cells) isolated from human donors can be injected into the mice to mimic the human in vivo immune response to a xenografted tumor. One such model was reported where colorectal cancer xenografts were implanted into NSG mice that had been injected with human PBMCs² and the effect of a combination of nivolumab (anti-PD1 therapy) and regorafenib (a multi-kinase inhibitor) was evaluated². Interestingly, the model was most predictive in an autologous setting where the tumor tissues and PBMCs were from the same patient as the allogeneic model showed nonspecific graft-vs-host issues². These results suggest that humanized models have a limited role in evaluating response to anticancer therapies and there is an unmet need for robust allogeneic humanized mouse models. Another type of transplant-based mouse model are syngeneic, where the mice with an intact immune system are injected with mouse tumor cells derived from mice with the same genetic background. Essentially, syngeneic models are mouse focused where a mouse tumor is evaluated in the context of a mouse immune system. While this model can be a useful proxy for the human state in some situations. Syngeneic models are reliable and cost-effective and can be used for short-lived efficacy studies. However, there are limited number of syngeneic cell lines and models and in many cases, limited translation to human disease.

Genetically modified mouse models (GEMMs) have been developed for decades and the first reported GEMM was in the 1980s³. The development of GEMMs has expanded rapidly as more advanced gene editing methods have been developed such as Cre-loxP, CRISPR-Cas9, RNA interference etc3. As gene editing methods have become more precise with less off-target effects, GEMMs have become more advanced and recapitulate several hallmarks of the disease state. However, developing GEMMs is an expensive and time-consuming exercise and in many cases, requires detailed knowledge of disease drivers. The genetic engineering required to build a relevant GEMM can be complicated with no guarantee of success. However, once a GEMM is successfully developed, it can be used to study disease development and progression, identify biomarkers for diagnostic use and prognostic monitoring and can be used to evaluate anticancer therapies. SEMMs or somatically engineered mouse models are another type of engineered model where somatic cells in the organ of interest are genetically engineered to express oncogenes or tumor suppressors⁴.

While there are several types of mouse models of cancer available, selecting the best model is not easy and requires a deep understanding of disease biology⁴. Multiple types of models may be used in a specific anticancer therapy development program that is dependent on the stage of drug development and the scientific questions that are being asked.

References:

¹https://www.nature.com/articles/s41416-019-0495-5

²https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532947/

³https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5286388/

⁴https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288236/

09 | Aug | 2023

Chinese

The primary organs that are impacted by drug toxicity are the liver, intestine and kidney that are the primary sites for waste generation and elimination. Drugs administered through various routes (oral, intravenous, intramuscular, etc.) are distributed throughout the body via the vasculature and are metabolized primarily in the liver and intestine before excretion via the kidney or rectum. Since the liver is typically the first organ exposed to a drug in the vasculature, it is the most vulnerable to drug induced toxicity and this effect is known as drug induced liver toxicity (DILI). DILI has a low incidence rate but is the reason for most cases of acute liver failure¹. In severe cases, a liver transplant may be the only therapeutic option. DILI can be segmented into intrinsic and idiosyncratic types² – intrinsic DILI is typically dose dependent and is based on the properties of the drug to cause damage to liver tissues. Intrinsic DILI is more predictable as information is available on the drug structure and function. Idiosyncratic DILI is less predictable and is not dose dependent and is believed to be cause by genetic variation among the human population.

Several models are currently available to detect DILI preclinically including animal models, 2D human hepatocytes, and 3D cell models that can include microfluidics. One of the major challenges with developing animal models to evaluate DILI is that the mechanism of toxicity of drugs is not always clear. Animal models to evaluate DILI caused by specific drugs have been developed – one example is the mouse model for acetaminophen induced DILI². Acetaminophen is a widely used pain medication which is known to cause liver failure with chronic use and was one of the earlier models of DILI. Typically, to measure DILI, drugs can be injected into rodent models at different doses to evaluate the extent of liver injury that is measured using specific biomarkers and evaluating changes in liver histology. This approach, while straightforward, does not address the question of how the drug causes liver injury². This is critical information that is needed for smarter drug design and improved next-generation therapeutics.

Increasingly, cell-based models are being used to study DILI as these systems are completely human and are increasingly becoming more complex and therefore, more predictive of the in vivo state. Cell-based models range from 2D cells to complex organ on chip systems. Human hepatocytes are considered to be the gold standard for evaluating hepatotoxicity in an in vivo setting, but it is difficult to source primary human hepatocytes. The HepG2 cell line has been used to form 3D spheroids, but those spheroids are generated from one cell type and do not represent the 3D microenvironment. An alternative source is induced pluripotent stem (iPS) cells that are differentiated into hepatocytes. Co-culture of hepatocytes with endothelial cells, stellate cells, and Kupffer cells can better recapitulate the native environment of the liver and have been used to evaluate DILI³.

Biomarkers to measure DILI can be broadly divided into two types – biochemical markers and genetic markers. Biochemical markers of DILI are typically measured in serum samples and range from common markers of liver damage such as glutamate dehydrogenase or cleaved K18 (keratin 18) to specific circulating microRNAs or miRs. miR-122 was shown to have some clinical relevance as a marker for DILI⁴. While several biomarkers for liver injury have been reported and evaluated, it has been a challenge to identify a comprehensive biomarker to reliably predict DILI across the board. The ideal biomarkers to measure DILI should be sensitive, reproducible and be truly predictive of DILI as opposed to transient variation in expression levels. Several biomarkers have shown significant variation in circulating biomarker levels across patient cohorts and in some cases, within the same individual sampled at different times⁴. Genetic markers of DILI are being explored primarily in the context of idiosyncratic DILI and the focus has been on HLA variants⁵. A recent publication has shown the correlation between specific HLA alleles and sensitivity to specific drugs but so far, no single HLA allele has been identified as a marker to predict DILI. GWAS (genome wide association studies) datasets are being used to identify non-HLA related genetic markers to predict DILI but so far, no significant biomarker has been identified. The challenge with identifying genetic markers is compounded by the low prevalence of DILI and the variation across populations. Nevertheless, the availability of GWAS data will continue to fuel the search for genetic markers to predict DILI in clinical trials.

References:

¹https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10031606/

²https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6478394/

³https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094553/#B109-ijms-24-06248

⁴https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288799/

⁵https://ascpt.onlinelibrary.wiley.com/doi/pdf/10.1111/cts.13424

10 | Jul | 2023

Chinese

The success rate of new therapies in the clinic is low as it is estimated that only 3.3% of cancer therapies were approved between 2000-2015¹. One of the main reasons for failure of many anti-cancer therapies is inter- and intra-patient tumor heterogeneity in morphology, gene expression pattern, metastatic potential, and mutational and epigenetic profiles. To understand these heterogeneities and identify candidates that are likely to fail early, more physiologically relevant preclinical cancer models are needed. 3D cell culture models are increasingly being used to evaluate new anti-cancer therapies largely due to the availability of cancer cell lines, primary patient tumors and patient tumors xenografts.

Patient-derived xenografts (PDX) are some of the most well-established models that are developed by direct implantation and expansion of primary human tumor samples into immunocompromised mice. PDXs retained the tumor native architecture so successful PDX models provide physiologically relevant source material for cell-based assays. While data from PDX models have translational relevance, they have some challenges – generating PDX in immunocompromised mice is not guaranteed and the process is time consuming and expensive.

Patient-derived Explants (PDEs) are ex vivo models where fresh tumors from biopsies or surgical resections are directly used for drug studies PDEs are generated using little to no tissue disruption and include tumor cells, stroma, immune cells, and vasculature, so they are an accurate microcosm of the native tumor environment². PDEs facilitate the interrogation of molecular and histological tumor characteristics in a single sample to construct a more complete picture of the tumor. However, PDEs can be extremely fragile and are liable to disintegrate rapidly and degrade over time so optimal culture conditions are necessary to obtain sufficient data. PDEs have several advantages and limitations compared to other 3D cell models. Since explants are generated from fresh tissue, they are more predictive of patient response, and the data generated from the explants can be correlated with the individual patient response. PDEs are a very useful model to study changes in immune cells in response to checkpoint inhibitors that are the primary drug targets for most tumor indications. PDEs have limitations primarily in terms of fresh tissue availability and the culture time frame. PDEs are not suited for longitudinal studies as they tend to start degrading in about 3 days, so there is a tight timeline to generate as much data as possible. Due to the short culture time, it is difficult to measure direct tumor killing effects of immunotherapies that can take several weeks to induce cytotoxicity. Despite these limitations, PDEs have a unique role in preclinical drug development of novel cancer therapies as they are the only model that truly represents the native tumor state.

Patient-derived organoids (PDOs) have become an established platform for preclinical validation of cancer drug assets. Primary tumor cell lines have been used to develop organoids that can be grown in a matrix that mimics the in vivo basement membrane. PDOs can be generated from small amount of patient tissues and can be grown and expanded to support drug screening and mechanism of action studies. Organoids cultured directly from patient samples can grow in days compared to PDX growth in animal models that can take several months. Additionally, PDOs are more efficient than PDXs in capturing the heterogeneity, polarity, cell-cell interactions, and structure of the native tumor³. However, PDOs have some limitations in that they do not fully recapitulate the tumor microenvironment and lack vasculature. To overcome those limitations primary tumor cells can be co-cultured with immune cells and cancer-associated fibroblasts⁴. Another limitation is that PDOs they may not represent the genetic heterogeneity of tumors, and it is possible that one clonal cell population that has a growth advantage will dominate the organoid. Despite limitations, PDOs are promising tools for disease modeling, gene therapy, understanding tumor growth and metastasis pathways, drug screening, and personalized and regenerative therapies, and evaluating the mechanism of action of single or combination therapies⁵.

References:

¹https://pubmed.ncbi.nlm.nih.gov/29394327/

²https://www.nature.com/articles/s41416-019-0672-6

³https://pubmed.ncbi.nlm.nih.gov/29587663/

⁴https://pubmed.ncbi.nlm.nih.gov/25185190/

⁵https://pubmed.ncbi.nlm.nih.gov/33291603/

22 | Jun | 2023

Chinese

Biologics drugs encompass a wide range of therapies including monoclonal antibodies, vaccines, protein and peptide therapeutics, cell therapies, viral vector gene therapies, nucleic acid (DNA and RNA) based therapies etc. These therapies are typically administered as injections or infusions and in specific cases via inhalation. Historically, new drugs have been primarily small molecules but increasingly new drug modalities such as cell and gene therapies and mRNA-based vaccines have increased from 11 to 21 percent of the drug development pipeline, which is the fastest growth seen in the sector¹. Along with pipeline growth, the approval of biologics therapies has increased. In 2021, CDER (Center for Drug Evaluation and Research) approved 50 new drugs of which 34% were monoclonal antibodies and biologics².

The growing pipeline and drug approvals requires increased high-quality manufacturing capabilities. It is important to note that manufacturing biologics for clinical trial and commercial use is challenging compared to chemically defined small molecule drugs. The manufacturing process requires high quality input material such as producer cell lines, sterility across the process and multiple testing points³ and scaling up a biologics manufacturing process can result in quality issues and insufficient supply to meet demand³. Due to the complexity of scale up manufacturing, drug developers are increasingly turning towards CDMOs or contract development and manufacturing organizations instead of building manufacturing facilities in-house.

CDMOs typically offer end-to-end services from drug development through manufacturing and filling and packaging the drug products. Due to the rapid growth in the biologics pipeline, the growth rate for biologics CDMO is expected to almost double from $9.9B in 2020 to $18B in 2026⁴. However, it is important to select the right CDMO and there are some key considerations for this selection process. It is critical to ensure that the CDMO has the right mix of personnel talent to support manufacturing of the drug modality of interest, and build a product development process that is scalable. Good CDMOs have a mix of top-tier scientists, technicians, process engineers and quality control personnel as well as other core functions. Another critical consideration is how much importance the CDMO places on quality assurance and compliance with regulatory requirements. Most CDMOs have product development and manufacturing capacity and processes in place but well reputed and experienced CDMOs will understand the critical importance of robust quality control systems that monitor and document every step in the manufacturing process. Along with quality control, CDMOs that have expertise in global regulatory requirements especially if the drug is going to be launched in multiple markets⁵. From a practical point of view, it is important for a CDMO to be engaged with the drug developer and communicate frequently especially if there are supply chain issues, manufacturing delays or process development challenges.

Due to the various considerations, biopharma companies often prefer to partner with a single CDMO partner who fits their needs. The partnership between a drug developer and CDMO is typically a long engagement so finding the right partner who is financially stable with an open transparent culture is critical for success. A recent survey of 50 drug developers highlighted the top 3 reasons to outsource to a biologics CDMO as risk mitigation, speed and access to a portfolio of skills⁶. Mitigating process development and manufacturing risks typically require the CDMO to have robust infrastructure, scientific expertise, robust quality control systems and regulatory compliance expertise. Available manufacturing capacity and personnel experience are key contributors to achieving program timelines.

In summary, selecting a CDMO requires going beyond manufacturing capacity and processes to ensure that a biologics drug manufacturing program is successful and achieves the timelines, scale and quality required for clinical trials and commercial use.

References:

¹https://www.contractpharma.com/issues/2023-01-02/view_columns/cdmo-trends-outlook

²https://www.mordorintelligence.com/industry-reports/biologics-contract-development-and-manufacturing-organization-cdmo-market

³https://www.statnews.com/2020/02/13/biologics-require-manufacturing-excellence-at-every-stage

⁴https://www.mantellassociates.com/blog/2021/08/the-rise-in-biologics-cdmo-market-value

⁵https://www.bioprocessonline.com/doc/is-selecting-a-cdmo-based-on-contract-price-really-saving-you-money-in-the-long-run-0001

⁶https://www.labiotech.eu/partner/outsource-early-clinical-pipeline-cdmo/

13 | May | 2023

Chinese

Temporal lobe epilepsy (TLE) is a chronic brain disorder where recurrent seizures occur in the temporal lobe. TLE can cause psychological issues, loss of short-term memory etc. and significantly impacts quality of life. Its incidence is reported to range between 0.04 to 0.1% of the global population and therefore is considered to be one of the more prevalent neurological diseases². TLE can result from multiple causes including, traumatic brain injury, cancer, stroke, infections or scarring in the hippocampus region¹. Current treatment paradigms include antiseizure medications, surgery and deep nerve stimulation³, but in some cases, seizures may not be fully managed with available therapies. Consequently, there is an ongoing need to develop improved therapies to manage TLE.

Mouse models of TLE that use pilocarpine or kainic acid to induce seizures have been used to study TLE⁴ and test therapies, but there are fundamental differences between rodent and human brains in terms of anatomy, physiology and function. Consequently, there is limited translatability from rodent data to human patients. Nonhuman primates (NHPs) are more physiologically relevant models to study TLE due to similarities in structure, function and neurochemical activity with the human brain. Interestingly, epilepsy can develop naturally in NHPs likely due to genetic factors or due to injury or infection, but can also be induced via a wide range of stimuli. Depending on the stimuli that is used, NHPs can develop generalized or focal epilepsy⁵. Focal epilepsy is induced via alumina gel, pilocarpine, kainic acid or electrical kindling, which uses an implanted stimulation electrode to induce seizures⁵. Relatively simple NHP epilepsy models have been used widely for the development of anti-seizure therapies. However, an unmet need is the development of new therapies for treatment refractory epilepsy, that need to be evaluated in more complex models that use a combination of stimuli to induce more refractory seizures. One such example, is the combination of pilocarpine and PTZ (pentylenetetrazol), where pilocarpine is used to induce an epilepsy phenotype and low doses of PTZ is used to trigger limbic seizures that are more frequent and severe⁶. In this model, available therapies reduced seizure intensity and frequency at varying degrees but did not completely suppress the seizures⁶. This data suggests that complex models that mimic treatment refractory epilepsy could be used to screen for more efficacious therapies.

Epileptic seizures are commonly detected using EEGs (electroencephalograms) where electrodes are positioned around the head to detect changes in brainwave activity. Apart from EEG analysis, diagnostic imaging such as PET, CT scan, MRI etc. are used to identify regions where there are changes in brain activity. Epilepsy patients typically undergo long term EEG scans where data is collected frequently over several days⁷. Manual analysis of this large dataset can take a long time, is prone to errors and needs to be done by a trained reader or experienced neurologists. Therefore, this type of analysis can be a significant bottleneck for the timely diagnosis and management of epilepsy. However, artificial intelligence (AI) can be a valuable aid for this analysis and can help reduce the error rate and time. In 2017, the Cleveland Clinic partnered with Google Inc. to develop a deep learning neural network to analyze a huge dataset (20 terabytes) from epilepsy patients⁷. The collaboration resulted in the development of a temporal graph convolutional network (TGCN) from the EEG data of 995 patients. This model combines spatial data over a set time period7 and showed impressive sensitivity and specificity7. Recently, a group at University College London developed an AI algorithm to identify areas of abnormal brain dysplasia that could lead to epileptic seizures using MRI data from 538 patients⁸. The algorithm was able to detect brain abnormalities in about 67% of the cases.

It is clear that AI is being actively used as a tool to identify and monitor epileptic seizures in human patients. However, it is important to reverse translate these AI algorithms to NHP epilepsy models so that AI and machine learning platforms can aid in the preclinical development of new therapies for treatment resistant epilepsy.

References:

¹https://www.healthline.com/health/temporal-lobe-epilepsy#causes

²https://www.sciencedirect.com/science/article/pii/S1525505022003997

³https://www.mayoclinic.org/diseases-conditions/temporal-lobe-seizure/diagnosis-treatment/drc-20378220

⁴https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4164293/

⁵https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779127/

⁶https://www.sciencedirect.com/science/article/abs/pii/S0920121116300985

⁷https://consultqd.clevelandclinic.org/deep-learning-models-for-automatic-seizure-detection-in-epilepsy/

⁸https://innovationdistrict.childrensnational.org/ai-algorithm-that-detects-brain-abnormalities-could-help-cure-epilepsy//