Insight
How Vorasidenib Might Change Brain Tumor Treatment
A pioneering drug holds the potential to revolutionize the treatment of a specific brain tumor commonly diagnosed in younger individuals. This revelation comes from a recent report in the New England Journal of Medicine.
The Promise of Vorasidenib
The drug in question, vorasidenib, has displayed remarkable efficacy in targeting tumors identified as "grade 2 IDH-mutant gliomas." When assessed in the INDIGO clinical trial, the drug notably delayed tumor growth, extending the average span from 11.1 months to a promising 27 months before signs of growth. Such a significant delay could mean a potential transformative change in treatment protocols for this type of brain tumor.
A New Perspective on Gliomas
Traditionally, gliomas were understood to be part of a spectrum. However, this view has evolved. The recent insights have illuminated that gliomas carrying the IDH gene mutation have a fundamentally different biological behavior. This variance not only impacts the prognosis but also reveals vulnerabilities that innovative treatments like vorasidenib can exploit.
This new understanding is especially significant considering that around 2,500 Americans, with a median age of only 40, are diagnosed with grade 2 IDH-mutant gliomas annually. The implications of these tumors are profound, often affecting cognitive functions, employment abilities, and other daily life aspects. Over time, these tumors tend to become unresponsive to available treatments, leading to fatal outcomes. The dominant approach has been a "watch and wait" strategy, delaying treatment until the tumor shows progression. However, vorasidenib might offer an alternative approach, potentially providing a proactive early treatment strategy.
INDIGO Trial Insights
In this groundbreaking trial, over 300 participants were randomly administered either vorasidenib or a placebo. This was a double-blind study, ensuring neither patients nor their doctors were aware of the treatment being given. The results were termed as "striking" with vorasidenib-treated patients not only living longer but also deferring the need for more aggressive treatments such as radiation and chemotherapy.
The Broader Landscape
While vorasidenib stands out as a promising candidate, it is essential to recognize the broader context. Servier Pharmaceuticals' $1.8 billion investment in Agios Pharmaceuticals' oncology division appears to be paying off. Vorasidenib, a dual inhibitor of mutant IDH1/2, was a notable asset in this acquisition, reflecting the potential significance of the drug. Recent news indicates that the drug improved progression-free survival rates among patients with residual or recurrent IDH mutant low-grade gliomas, achieving its primary endpoint. The trial results also outperformed the placebo in key secondary outcomes.
Looking ahead, with accelerated results from the phase 3 analysis and faster than anticipated enrollment, Servier is advancing ahead of its timeline. However, they are yet to determine the exact timelines for seeking approval. If they meet the criteria, a $200 million milestone payment is set to be triggered, providing a financial boost for further research and development.
In Conclusion
The emergence of vorasidenib has instilled a renewed sense of hope for patients with grade 2 IDH-mutant gliomas. With a potential to redefine treatment approaches and deliver the first targeted therapy for low-grade gliomas, the drug represents a significant stride in brain tumor research and treatment. However, as with all breakthroughs, a careful, patient-centric approach will be essential to ensure the best outcomes.
Leqembi: The High Cost of Hope in Alzheimer's Disease Amidst Efficacy and Safety Concerns
Sasan Dastaran, PhD, MBA
05/14/2023
The recent approval of Leqembi (lecanemab), an innovative Alzheimer's medication developed by Eisai and Biogen, has ignited a blend of optimism and debate within the medical fraternity. While Leqembi offers a glimmer of hope by mildly decelerating the progression of early Alzheimer's dementia, its substantial annual cost of $26,500 has provoked scrutiny over its genuine efficacy and worth. The Institute for Clinical and Economic Review (ICER) has estimated a health-benefit price benchmark (HBPB) for Leqembi to be between $8,900 and $21,500 per year, indicating that a discount of 19% to 66% would align it with cost-effectiveness thresholds.
Navigating the Complex Terrain of Alzheimer's Drug Discovery
The path to Alzheimer's drug discovery is strewn with obstacles. The disease's extended preclinical phase, potentially lasting decades before symptoms manifest, complicates early detection and intervention. Additionally, the precise aetiology of Alzheimer's remains a mystery. Although the disease is associated with the accumulation of amyloid-beta plaques and tau tangles in the brain, the connection between these phenomena and neuron death, leading to cognitive decline, is yet to be fully understood.
Moreover, several high-profile disappointments in Alzheimer's drug discovery, such as anti-Aβ small molecules like Verubecestat and Atabecestat by Merck and Janssen respectively, and anti-Aβ MABs, Aducanumab (Aduhelm), and Eli Lilly's Solanezumab, have resulted from attempts to target these underlying disease mechanisms. Despite these setbacks, there are promising contenders in the pipeline, including Eli Lilly’s Donanemab, Merck’s Ublibetasad (MK-8931), and ALZ-801 by Alzheon in phase III.
Leqembi's Approval and the Ensuing Efficacy Debate
Leqembi's approval hinged on the outcomes of the Clarity AD clinical trial, which showcased a reduction in brain amyloid and a deceleration in cognitive decline in Alzheimer's patients. In this trial, cognition and function were gauged using the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). The average change from the baseline at 18 months was 1.21 with Leqembi and 1.66 with placebo, a disparity often interpreted as a 27% slower cognitive decline in the Leqembi group. However, ICER analysts have expressed scepticism over whether amyloid removal is a suitable surrogate outcome for clinical benefit. ICER researchers have previously stated that the data is insufficient to confirm that Leqembi’s amyloid removal slows cognitive decline.
Safety Concerns
Leqembi's safety profile raises concerns due to potential brain bleeding or swelling. Reports of three fatalities among patients who received the drug are alarming. Eisai refutes any connection to Leqembi, but the FDA issued a severe safety warning. The FDA's full approval of Leqembi brings hope, but long-term evaluation of its efficacy and safety is imperative.
The AI Effect: Reshaping the Landscape of Clinical Trials
Leveraging the power of artificial intelligence, a breakthrough platform has expedited the process of patient recruitment for clinical trials. This platform wades through extensive patient medical data, identifying matches for clinical trial requirements. What was once a protracted and complex process has now been streamlined and accelerated.
AI's role in clinical trials has seen a steady expansion over the years, incorporating advanced technologies such as machine learning (ML), natural language processing (NLP), and deep learning algorithms. These technological advancements have played a significant role in refining the functions of AI in clinical trials.
AI has made a notable impact in areas such as patient recruitment and trial monitoring. Innovative tools like Criteria2Query and DQueST have optimized these processes, enhancing both efficiency and accuracy. These tools sift through intricate databases, decoding trial eligibility criteria into layman's language, easing the burden on both medical professionals and patients. Furthermore, ML algorithms have been crucial in predicting patient dropout rates, paving the way for better resource distribution and trial management.
AI's influence on clinical trials is set to increase in the future. The rapid progression in AI technology signals improved patient recruitment strategies, personalized treatment plans, and more effective data management methods. Additionally, AI presents innovative solutions for enhancing technical infrastructure, patient monitoring systems, and clinical endpoint detection — all pivotal aspects of successful clinical trials.
The key to unlocking these advantages is a collaborative effort from academic, industrial, and regulatory bodies. Partnerships need to be encouraged, and suitable funds should be allocated for research and pilot studies to validate AI applications. To foster public trust, health professionals should be well-versed with these digital tools, advocating digital literacy initiatives.
Takeda's $4 Billion Bet on an AI-Discovered Candidate for Psoriasis
In a landmark move, Japan's Takeda Pharmaceutical Co. invested $4 billion to acquire an experimental psoriasis drug from Nimbus Therapeutics, a Boston-based startup. The deal stands out not just for its hefty price tag, but for the revolutionary technology used to select the drug: artificial intelligence (AI). This AI-driven selection process took a mere six months, in stark contrast to the years typically required for traditional drug discovery.
This audacious step from Takeda is reflective of a broader trend within the global pharmaceutical industry, which is increasingly embracing AI and machine learning to accelerate drug discovery and development. Over the next decade, the application of AI in early-stage drug development could potentially yield an additional 50 novel therapies, translating into more than $50 billion in sales, according to Morgan Stanley.
Deep Pharma Intelligence, a research firm, reports that investments in AI-powered drug discovery companies have tripled over the past four years, hitting $24.6 billion in 2022. Leading pharmaceutical companies like Bayer, Roche Holding, and Takeda are partnering with AI-focused firms such as Recursion Pharmaceuticals, Exscientia Plc, and BenevolentAI, to expedite and revolutionize drug discovery through machine learning.
The industry's interest in AI was sparked in 2018 when Alphabet Inc.’s DeepMind used AlphaFold, an AI program, to predict protein shapes faster than a biologist. This breakthrough accelerated the drug discovery process and increased profit potential, considering the high cost of traditional drug development.
The Covid-19 pandemic further accelerated AI adoption in the search for treatments. Pfizer Inc. collaborated with AI drug discovery firm XtalPi Inc. to speed up the chemical formulation of its Covid-19 pill, Paxlovid. Both Paxlovid and Pfizer's Covid-19 vaccine, Comirnaty, were approved by the US Food and Drug Administration in under two years, a fraction of the typical decade-long process.
AI and computational methods are transforming drug discovery and pharmaceutical operations. Takeda, for instance, employs over 500 quantitative scientists and tech experts who use AI and machine learning to identify the best molecules for targeting proteins and to understand disease characteristics and variations among patient populations.
However, with these advancements come ethical, legal, and safety concerns. Biases in the data used to create algorithms can impact the clinical recommendations they generate. Ensuring transparency in model development and validation is a key step towards mitigating these risks.
#pharma #drugdevelopment #drugdiscovery #ai #takeda #limack #psoriasis #pfizer
Revolutionizing Alopecia Areata Treatment: The Promise of Ritlecitinib and its Competitors
Alopecia Areata (AA) is a challenging condition that causes hair loss on the scalp and other parts of the body. Fortunately, a promising new treatment option is emerging in the form of ritlecitinib, a Janus kinase (JAK) inhibitor developed by Pfizer. This breakthrough treatment works by blocking the activity of enzymes that contribute to hair loss and inflammation, both of which are characteristic features of AA. Clinical trials have shown that ritlecitinib can induce hair regrowth in patients with moderate-to-severe AA, making it a promising new option for AA treatment.
However, ritlecitinib is not the only drug in development for AA treatment. Other JAK inhibitors and drugs that target the immune system or stimulate hair follicle growth are also showing promising results. While there are limitations to consider, such as side effects and cost, the potential benefits of these new treatments are significant.
As the medical community continues to make progress in the development of AA treatments, patients have reason to hope for improved outcomes and a better quality of life. The key to success is to work closely with healthcare providers to determine the most appropriate treatment plan for each individual case.
AI in Drug Discovery: A Comparative Snapshot
AI and computational techniques are the new frontiers in drug discovery, offering groundbreaking approaches to speed up and enhance the process. These methods, such as Supervised Learning and Unsupervised Learning, utilize machine learning to make accurate predictions and identify patterns in data.
Supervised Learning is a method where AI learns from labelled data to make predictions or decisions without being explicitly programmed to perform the task. It's effective for specific tasks and works well with labelled data, but its requirement for such data can be a limitation.
Unsupervised Learning, on the other hand, can identify patterns and relationships among data points without the need for labelled data. It's great for data exploration and analysis, although its applications might be limited in comparison to Supervised Learning.
Other techniques, like Convolutional Neural Networks, Recurrent Neural Networks, and Transformer-based Models, delve into the realm of Deep Learning. They are effective in handling complex data and learning intricate patterns, though they might require significant computational resources and pose interpretability challenges.
Computational methods like Molecular Dynamics Simulations, Docking and Scoring, and QSAR Models provide detailed and interpretable results, though they may require specialized knowledge and can be computationally expensive.
All these techniques play crucial roles in various stages of drug discovery, from target prediction to lead optimization. They have their unique strengths, limitations, and developmental opportunities, as outlined in the table. Understanding these techniques can help us navigate the ever-evolving landscape of AI-driven drug discovery.
Revolutionizing Drug Discovery: The Power of AI
Dr Sasan Dastaran
Artificial Intelligence (AI) has emerged as a game-changer in the field of drug discovery and development, overcoming key challenges and bottlenecks in the traditional drug development process. This new generation of AI companies is focusing on three critical failure points in the drug development pipeline: picking the right target in the body, designing the right molecule to interact with it, and determining which patients that molecule is most likely to help [1].
Picking the right target in the body
AI-driven target identification leverages large-scale biological data and advanced algorithms to find the most promising drug targets. A recent example is AstraZeneca's collaboration with AI company BenevolentAI, which led to the identification of a new target for idiopathic pulmonary fibrosis, a lung disease with high unmet medical needs [2].
Designing the right molecule to interact with the target
AI-enhanced molecule design involves the use of deep learning models, such as generative adversarial networks (GANs) and transformer-based models like molecular transformers, to create new molecules with desired properties [3]. For instance, Insilico Medicine used a GAN to generate a new drug candidate for fibrosis, which entered preclinical trials in just 18 months, a process that traditionally takes several years [4].
Determining which patients the molecule is most likely to help
AI-driven precision medicine enables the identification of patient subpopulations most likely to benefit from a specific drug. In a recent example, researchers used machine learning algorithms to analyze genomic data from Alzheimer's patients, which led to the identification of specific subgroups and potential new therapeutic targets [5].
AI-enhanced drug discovery has the potential to significantly reduce the cost, time, and risk associated with drug development. By 2025, the AI drug discovery market is projected to reach $3.5 billion, up from $230 million in 2020 [6]. As the technology continues to advance, the impact of AI on drug discovery is expected to grow exponentially, ultimately revolutionizing the way new drugs are discovered and brought to market.
References:
[1] McKinsey & Company. (n.d.). How AI could revolutionize drug discovery. Retrieved from https://www.mckinsey.com/industries/life-sciences/our-insights/how-ai-could-revolutionize-drug-discovery
[2] Nature. (2022). Machine learning identifies new drug targets for idiopathic pulmonary fibrosis. Retrieved from https://www.nature.com/articles/s42256-022-00465-9
[3] Chemical Science. (2023). The molecular transformer for molecule optimization. Retrieved from https://pubs.rsc.org/en/content/articlehtml/2023/sc/d2sc05709c
[4] Morgan Stanley. (n.d.). AI Takes on Drug Discovery. Retrieved from https://www.morganstanley.com/ideas/ai-drug-discovery
[5] Journal of Prevention of Alzheimer's Disease. (2023). Machine learning identifies patient subgroups and new targets for Alzheimer's disease. Retrieved from https://link.springer.com/article/10.14283/jpad.2023.1
[6] Neuroscience News. (2022). Artificial intelligence continues to revolutionize drug discovery. Retrieved from https://neurosciencenews.com/ai-drug-discovery-23150/
The Blockbuster Battle: Expanding SGLT2 Inhibitors Beyond Diabetes to Heart Failure and Chronic Kidney Disease
Dr Sasan Dastaran
SGLT2 inhibitors, a class of oral antidiabetic drugs, work by blocking the sodium-glucose cotransporter-2 in the kidneys, which leads to glucose excretion in the urine and lowers blood sugar levels in patients with type 2 diabetes. In 2019, Eli Lilly and Boehringer Ingelheims' Jardiance (empagliflozin) led the SGLT2 inhibitor class with sales of nearly $3 billion. Given that Jardiance had a slightly better side effect profile than AstraZeneca's Farxiga, AZ pursued an indication expansion strategy that made Farxiga the first SGLT2 inhibitor approved for systolic heart failure independent of patients' diabetes status in 2020. This development increased Farxiga's sales by 49% to $3 billion in 2021, while Jardiance reached sales of $5.8 billion and continued to grow in its base type 2 diabetes market. In 2021, Farxiga secured another approval for CKD, again independent of patients' diabetes conditions, providing it with a unique competitive edge in three separate therapeutic areas and pushing its sales to $4.3 billion by the end of 2022.
Farxiga's CKD approval relied on the DAPA-CKD clinical trial results, which demonstrated the drug significantly reduced kidney function decline, kidney failure, and cardiovascular events in patients with or without type 2 diabetes. With around 850 million people affected by CKD globally and 90% of cases remaining undiagnosed, there is a substantial market opportunity for Farxiga if AstraZeneca can facilitate diagnosis through healthcare professionals and patient education.
Although Jardiance is pursuing CKD approval, it has already achieved nearly $8 billion in combined sales (from Eli Lilly and Boehringer Ingelheim) by the end of 2022, thanks to its diabetes and heart failure indications. By leveraging real-world evidence and AI, pharmaceutical strategists can explore new opportunities when facing challenges in specific therapeutic areas, such as targeting new indications, overcoming regulatory hurdles, and predicting safety concerns. Expanding the use of drugs like SGLT2 inhibitors to new therapeutic areas not only strengthens their market position but also benefits patients by offering improved treatment options and potentially reducing hospitalization rates.
GSK's Acquisition of Zejula: Risks and Potential Rewards
The market for PARP inhibitors, a promising class of cancer therapies, has become increasingly crowded, leading to robust competition for certain indications and legal disputes like the recent patent case between GSK and AstraZeneca over Zejula. GSK's acquisition of Tesaro, which included Zejula, for $5.1 billion presented both risks and potential rewards. AstraZeneca's Lynparza has triumphed in the battle to widen indications so far, but the question remains: who will ultimately claim victory in this market? In this article, we will discuss the challenges and opportunities that arose from GSK's acquisition of Tesaro and the lessons that can be learned from the experience.
Risks and Challenges
Intense competition: The crowded PARP inhibitor market meant that GSK's Zejula faced strong competition from other established drugs, such as AstraZeneca's Lynparza and Clovis Oncology's Rubraca. This competition may have made it difficult for Zejula to secure market share and achieve growth.
Legal disputes: GSK's acquisition of Tesaro led to a patent dispute with AstraZeneca, highlighting the risks associated with entering a highly competitive market and the importance of thorough due diligence.
Narrow patient population: Zejula's initial focus on patients with specific genetic mutations limited the potential patient population, making it challenging to achieve significant market share.
Potential Rewards
Bolstering GSK's presence in oncology: The acquisition of Tesaro allowed GSK to strengthen its presence in the oncology space and capitalize on the growing interest in personalized medicine.
Expanding indications: Zejula offered potential for expansion into new indications, such as prostate and pancreatic cancers, which could increase its market share and improve patient outcomes.
Combination therapies: The development of effective combination therapies, such as pairing Zejula with immunotherapies or chemotherapy, could lead to improved patient outcomes and increased market share.
Lessons from GSK's Acquisition of Tesaro
Importance of due diligence: Thoroughly assess the competitive landscape, including regulatory hurdles, market access, and potential legal disputes, as well as the target company's pipeline and product performance. GSK's acquisition was based on the belief that Zejula had significant growth potential, but the complexities of the PARP inhibitor market may have been underestimated.
Value of a robust pipeline: A diversified portfolio of drug candidates can help mitigate risks associated with a single product. Tesaro's pipeline, with Zejula at the forefront, was one of the main drivers behind the acquisition. However, its setbacks highlighted the importance of a comprehensive pipeline.
Strategic collaborations and partnerships: GSK's experience with Tesaro demonstrates that strategic partnerships and collaborations can play a crucial role in overcoming challenges in the market and unlocking the full potential of acquired assets.
Managing expectations: Companies should remain realistic and cautious in their projections, avoiding overpromising on a drug's potential. While Zejula was initially touted as a potential blockbuster, its setbacks underscore the need to manage expectations and communicate potential risks transparently.
Agility and adaptability: In a rapidly evolving market, companies must be prepared to adapt their strategies and seize new opportunities. GSK's continued efforts to expand Zejula's indications and explore combination therapies demonstrate the importance of being nimble and responsive to market changes.
Conclusion
GSK's acquisition of Tesaro and its subsequent challenges with Zejula serve as an important lesson for companies considering entering the competitive PARP inhibitor market. Thorough due diligence, a robust pipeline, and strategic partnerships are essential for navigating the risks and capitalizing on the potential rewards of such acquisitions.
Sasan Dastaran, MBA, PhD
Navigating the Crowded PARP Inhibitor Market: Challenges, Opportunities, and Key Players
Poly (ADP-ribose) polymerase (PARP) inhibitors have emerged as a promising treatment option for various cancers, particularly those with BRCA1 and BRCA2 gene mutations, which are commonly found in ovarian, breast, and prostate cancers. These targeted therapies have the potential to revolutionize cancer treatment by offering personalized approaches to patients with specific genetic mutations. In this article, we explore the market opportunities, key players, and recent data on the effectiveness of PARP inhibitors in cancer treatment.
Current Challenges in the PARP Inhibitor Market
Competition: The market has become crowded with multiple PARP inhibitors available from different pharmaceutical companies, leading to intense competition. This competition can create challenges in securing market share and achieving growth.
Patient Acquisition: The competition among PARP inhibitors and the focus on patients with specific genetic mutations make patient acquisition difficult. The need for genetic testing and the relatively small patient population with BRCA1/2 mutations contribute to this challenge.
Resistance: The development of resistance to PARP inhibitors can limit their clinical utility and effectiveness. Understanding the mechanisms of resistance and developing strategies to overcome them are significant challenges faced by the industry.
Combination Therapies: Identifying effective combination therapies, such as combining PARP inhibitors with immunotherapies or chemotherapy, has been challenging. Developing these combinations is crucial for improving patient outcomes and expanding the market for PARP inhibitors.
Opportunities
Expanding Indications: Initially, PARP inhibitors gained traction as a treatment option for ovarian cancer with BRCA mutations. Their use has since expanded to include other cancer types such as breast, prostate, and pancreatic cancers. Further research into the efficacy of PARP inhibitors in additional cancer types presents significant market opportunities for pharmaceutical companies.
Personalized Medicine: As the healthcare industry shifts towards personalized medicine, there is increasing demand for targeted therapies like PARP inhibitors. Genetic testing for BRCA mutations and other DNA repair deficiencies can help identify patients more likely to benefit from PARP inhibitor treatment, creating a more personalized and effective approach to cancer therapy.
Combination Therapies: Combining PARP inhibitors with other cancer treatments, such as chemotherapy, immunotherapy, or other targeted therapies, has shown promising results in clinical trials. Developing effective combination therapies could lead to improved patient outcomes and increased market share for PARP inhibitors.
Enhanced Drug Development: As more is understood about the mechanism of action and potential benefits of PARP inhibitors, there is an opportunity for pharmaceutical companies to develop next-generation PARP inhibitors with improved efficacy, safety, and dosing regimens. Additionally, research into biomarkers that can predict patient response to PARP inhibitors can further optimize treatment strategies.
Global Market Growth: The global market for PARP inhibitors is expected to grow significantly due to increased awareness, more robust clinical data, and regulatory approvals in various regions. As more countries approve the use of PARP inhibitors for different cancer types, there will be a substantial opportunity for market expansion and revenue generation.
Key Players
Key players in the PARP inhibitor market include AstraZeneca with Olaparib (Lynparza), GlaxoSmithKline (GSK) with Niraparib (Zejula), Clovis Oncology with Rucaparib (Rubraca), Pfizer with Talazoparib (Talzenna), AbbVie with Veliparib, and Junshi Biosciences/IMPACT Therapeutics with Senaparib. These companies are striving to maintain and expand their presence in the competitive PARP inhibitor market by capitalizing on the opportunities and addressing the challenges. (Table 1)
Conclusion
The PARP inhibitor market is becoming increasingly crowded, with multiple key players competing for market share. While challenges exist in patient acquisition, competition, resistance, and the development of combination therapies, significant opportunities remain for pharmaceutical companies to expand indications, develop personalized medicine approaches, and create more effective combination therapies. As the market for PARP inhibitors continues to grow, key players like AstraZeneca, GSK, Clovis Oncology, Pfizer, AbbVie, and Junshi Biosciences/IMPACT Therapeutics will need to capitalize on these opportunities and address the challenges to maintain and grow their presence in this competitive space. Continuous research, development of next-generation inhibitors, and enhanced understanding of PARP inhibitors' mechanism of action will be crucial in shaping the future landscape of the market. With the increasing focus on personalized medicine, PARP inhibitors hold significant potential to revolutionize cancer treatment and improve patient outcomes.
Sasan Dastaran, MBA, PhD
Born from serendipity and later recognized with a Nobel Prize, oligonucleotide therapies have unlocked a new realm of possibilities for treating previously intractable diseases. Capable of targeting over 10,000 once "undruggable" proteins produced by now "druggable" mRNAs, these therapies offer hope for a wide range of conditions, from genetic disorders to cardiac diseases. Here's a brief summary of the key information on oligonucleotide therapies.
There are three primary types of oligonucleotide therapies, with 15 therapeutics currently on the market and an expected market value of $6.53 billion by 2030
1- Antisense oligonucleotides (ASOs) are single-stranded DNA or RNA molecules that bind to specific mRNA sequences, blocking translation or promoting mRNA degradation. Approved therapies include Nusinersen for spinal muscular atrophy, Inotersen for hereditary transthyretin amyloidosis, and mipomersen for homozygous familial hypercholesterolemia, with clinical trials underway for Duchenne muscular dystrophy, Huntington's disease, and amyotrophic lateral sclerosis.
2- Small interfering RNAs (siRNAs) are double-stranded RNA molecules that trigger RNA interference (RNAi), degrading specific mRNA sequences. Patisiran, an approved therapy for hereditary transthyretin-mediated amyloidosis, leads the way, with clinical trials exploring treatments for hepatitis B, hypercholesterolemia, and age-related macular degeneration.
3- MicroRNA mimics or inhibitors are small, non-coding RNA molecules that regulate gene expression by binding to target mRNAs and causing their degradation. While there are no approved therapies yet, clinical trials are targeting diseases such as cancer, liver fibrosis, and heart failure.
#personalizedmedicine #oligonucleotides #cancer #medicine #pharma #lifesciences #genetherapy #Consulting
Sasan Dastaran, MBA, PhD