Investment Implications with The Rise of AI in Healthcare

June 18, 2024

Artificial intelligence (AI) is transforming the healthcare industry in unprecedented ways. From enhancing patient care and accelerating drug discovery to improving clinical decision-making, AI's integration is revolutionizing various aspects of healthcare. The significance of AI lies in its ability to process vast amounts of data quickly and accurately, providing insights that were previously unattainable. This capability is not only advancing medical research and treatment but also creating new avenues for efficiency and precision in healthcare delivery.


The rise of AI in healthcare has captured the attention of investors, entrepreneurs, and industry experts alike. As AI technologies continue to evolve and demonstrate their potential, investment in AI-driven healthcare solutions has surged. These investments are driving innovation and offering significant returns, making AI one of the most promising areas for growth in the healthcare sector.


In this article, we explore the investment implications of AI in healthcare. We will highlight promising applications, uncover opportunities, and address the challenges that come with investing in this rapidly evolving field. By understanding the landscape of AI in healthcare, investors can make informed decisions and capitalize on the transformative potential of these technologies.


AI-Driven Precision Medicine: A Game-Changer


Precision medicine represents a groundbreaking shift in the healthcare paradigm by tailoring treatments to individual patients based on their unique genetic, molecular, and clinical profiles. Unlike traditional approaches that apply a one-size-fits-all method, precision medicine aims to deliver personalized healthcare solutions that enhance the efficacy and safety of treatments. This personalized approach is particularly crucial in fields such as oncology, where genetic differences among patients can significantly impact how they respond to therapies.


AI plays a pivotal role in advancing precision medicine. By leveraging sophisticated algorithms and machine learning techniques, AI can analyze vast amounts of patient data, including genomic information, clinical records, and real-world evidence. This analysis enables AI to identify patterns and make predictions about disease progression, treatment responses, and potential adverse events. As a result, healthcare providers can develop more effective, individualized treatment plans that improve patient outcomes and reduce the trial-and-error approach often associated with conventional treatments.


A prime example of AI-driven precision medicine is the work being done by Notable Labs. This clinical-stage platform therapeutics company is at the forefront of developing predictive precision medicines using advanced AI and machine learning. Notable Labs' approach involves analyzing patient-derived cancer cells' functional responses to various drugs. By applying AI to these data, they can identify the most effective therapeutic regimens tailored to each patient's unique molecular profile. This innovative approach not only enhances the precision of cancer treatments but also accelerates the development of targeted therapies, offering new hope to patients with difficult-to-treat cancers.


The benefits of AI-driven precision medicine are substantial. Improved patient outcomes are perhaps the most significant advantage, as personalized treatments are more likely to be effective and less likely to cause adverse effects. Additionally, by targeting therapies more precisely, healthcare providers can reduce unnecessary treatments and associated costs, making healthcare more efficient and cost-effective. Furthermore, the accelerated development of targeted therapies means that new, life-saving treatments can reach patients faster, potentially transforming the landscape of disease management and patient care.


Investment Opportunities in AI Healthcare Startups


The integration of AI in healthcare has generated a wealth of investment opportunities, particularly within startups driving innovation across various sectors of the industry. Venture capitalists, strategic investors, and angel investors are increasingly focusing on AI-driven healthcare startups that are revolutionizing drug discovery, diagnostics, clinical decision support, and patient monitoring.


Drug Discovery

AI is playing a transformative role in accelerating the drug discovery process. Traditional drug development is notoriously lengthy and costly, often taking over a decade and billions of dollars to bring a new drug to market. AI can streamline this process by analyzing vast datasets to identify potential drug targets, predict the efficacy and safety of candidate molecules, and optimize clinical trial designs.


  • Atomwise: This company uses AI to discover new drugs, employing deep learning algorithms to predict bioactivity and toxicity. Atomwise's technology has already been utilized to identify promising drug candidates for diseases like Ebola and multiple sclerosis.


  • Insilico Medicine: Known for its AI-driven drug discovery platform, Insilico Medicine integrates genomics, proteomics, and pharmacogenomics data to uncover novel therapeutic targets and develop personalized medicine approaches.


  • Recursion Pharmaceuticals: This company combines AI with experimental biology to identify new treatments for various diseases. Recursion's platform automates high-content screening of millions of cellular images to find potential drug candidates rapidly.


Diagnostics

AI-powered diagnostic tools are revolutionizing the way medical conditions are detected and diagnosed. These tools can analyze medical images and other diagnostic data with greater accuracy and speed than human radiologists, leading to earlier and more precise diagnoses.


  • Viz.ai: Specializing in stroke detection, Viz.ai uses deep learning to analyze CT scans and identify signs of a stroke in real time. This rapid diagnosis can significantly improve patient outcomes by facilitating timely treatment.


  • Zebra Medical Vision: Zebra Medical Vision employs AI algorithms to read medical imaging scans and detect various diseases, including liver disease, osteoporosis, and cardiovascular conditions. Their AI platform can analyze these images faster and often more accurately than traditional methods.


  • Aidoc: This company provides AI solutions for radiology, analyzing medical images to detect critical conditions like intracranial hemorrhages and pulmonary embolisms. Aidoc's tools assist radiologists in prioritizing urgent cases and improving diagnostic accuracy.


Clinical Decision Support

AI-driven clinical decision support systems are enhancing the capabilities of healthcare professionals by providing more informed and accurate diagnoses, treatment recommendations, and risk assessments. These systems use data from electronic health records, medical literature, and patient data to assist clinicians in making better-informed decisions.


  • Babylon Health: Babylon Health uses AI to provide virtual healthcare services, including symptom checking and telemedicine consultations. Their AI-powered platform helps patients understand their symptoms and connects them with appropriate healthcare providers.


  • Buoy Health: This company offers an AI-driven platform that guides patients through a series of questions about their symptoms to provide personalized health information and next steps. Buoy's tool aims to improve patient engagement and decision-making.


  • Infermedica: Infermedica's AI-based platform supports clinical decision-making by providing symptom analysis, preliminary diagnosis, and triage recommendations. It helps healthcare providers manage patient intake and prioritize care.


Patient Monitoring

AI algorithms are enhancing patient monitoring by analyzing real-time data from wearable devices, remote monitoring systems, and electronic health records. This capability is particularly beneficial for managing chronic conditions and providing preventive care.


  • Wearable Devices: AI-powered wearables like smartwatches and fitness trackers continuously monitor vital signs and health metrics, alerting users and healthcare providers to any concerning changes. Examples include devices from companies like Fitbit and Apple.


  • Remote Monitoring Systems: AI-driven remote monitoring systems collect data from patients with chronic conditions, such as diabetes or heart disease, and analyze it to detect early signs of deterioration. These systems provide personalized recommendations for lifestyle modifications and preventive care, reducing hospital readmissions and improving patient outcomes.


The rise of AI in healthcare presents lucrative opportunities for investors. By focusing on startups that leverage AI to innovate in drug discovery, diagnostics, clinical decision support, and patient monitoring, investors can capitalize on the transformative potential of these technologies. As AI continues to reshape the healthcare landscape, the value and impact of these investments are likely to grow significantly.


Challenges and Considerations


While the investment opportunities in AI healthcare startups are promising, several challenges and considerations must be addressed to ensure successful and sustainable growth in this sector.


Regulatory Landscape

The regulatory environment for AI-based healthcare products is complex and continuously evolving. Regulatory bodies such as the FDA in the United States and the EMA in Europe are still developing comprehensive frameworks for approving AI-driven medical technologies. Navigating these approval processes can be intricate and time-consuming, posing a significant challenge for startups.


  • Complexity and Evolving Nature: The regulatory landscape is characterized by its complexity and ongoing changes. New guidelines and standards are regularly introduced to address the unique aspects of AI technologies in healthcare.


  • Importance of a Solid Regulatory Strategy: Startups must develop a robust regulatory strategy that aligns with current regulations and anticipates future changes. This involves engaging with regulatory bodies early in the development process and ensuring compliance with all necessary guidelines to facilitate smoother approval pathways.


Data Quality and Privacy

AI algorithms rely heavily on high-quality data for training and accurate predictions. The quality and quantity of data directly impact the performance and reliability of AI healthcare solutions.


  • Dependence on High-Quality Data: Ensuring that the data used to train AI models is comprehensive, accurate, and representative is crucial. Poor data quality can lead to biased or incorrect predictions, undermining the effectiveness of AI applications in healthcare.


  • Ensuring Data Privacy, Security, and Compliance: Healthcare data is highly sensitive, and maintaining its privacy and security is paramount. Compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe is essential. Startups must implement robust data protection measures and ensure transparency in data usage to build trust with patients and healthcare providers.


Ethical Considerations

The use of AI in healthcare raises important ethical concerns that must be addressed to promote responsible AI development and deployment

.

  • Addressing Bias, Transparency, and Accountability: AI systems can inadvertently perpetuate biases present in the training data, leading to unfair or discriminatory outcomes. Ensuring transparency in AI decision-making processes and establishing accountability mechanisms is crucial to address these issues.


  • Importance of Responsible AI Development: Startups must prioritize ethical considerations in their development processes. This includes implementing fairness checks, conducting bias audits, and promoting diversity in AI research and development teams to ensure that AI solutions are equitable and trustworthy.


Adoption and Integration

Integrating AI solutions into existing healthcare systems can be challenging due to various factors, including resistance to change and the need for healthcare professionals to develop new skills and workflows.


  • Challenges in Integration: Healthcare systems are often complex and fragmented, making the integration of new AI technologies a significant challenge. Compatibility with existing electronic health records (EHRs) and interoperability with other systems are critical factors for successful integration.


  • Factors Like Resistance to Change and Need for New Skills: Healthcare professionals may resist adopting AI solutions due to concerns about job displacement, reliability, and the need for new skills. Comprehensive training programs and clear communication about the benefits of AI can help mitigate resistance and facilitate smoother adoption.


Scalability and Commercialization

Scaling AI healthcare solutions from pilot projects to widespread commercialization requires substantial resources, strategic planning, and partnerships.


  • Scaling AI Healthcare Solutions: Moving from successful pilot projects to full-scale implementation involves overcoming various technical, logistical, and financial challenges. Ensuring that AI solutions can handle increased workloads and maintaining their performance at scale is essential.


  • Resources, Partnerships, and Strategic Planning Required: Achieving scalability and commercialization often requires significant investment, strategic partnerships, and meticulous planning. Collaborating with established healthcare providers, payers, and technology companies can provide the necessary infrastructure and market access to scale AI solutions effectively.


Navigating these challenges and considerations is crucial for investors and startups alike. By addressing regulatory, data privacy, ethical, adoption, and scalability issues proactively, stakeholders can maximize the potential of AI in healthcare and drive meaningful advancements in patient care and clinical outcomes.


Notable Labs: A Pioneer in AI-Driven Precision Medicine


Notable Labs stands at the forefront of the AI-driven precision medicine revolution, exemplifying the transformative potential of artificial intelligence in healthcare. As a clinical-stage platform therapeutics company, Notable Labs leverages advanced AI and machine learning techniques to develop predictive precision medicines tailored to individual patients' unique genetic, molecular, and clinical profiles.


Precision Medicine in Action

At the heart of Notable Labs' innovation is their Predictive Medicine Platform (PMP), which utilizes sophisticated AI algorithms to analyze vast amounts of patient data. This includes genomic information, clinical records, and real-world evidence. By identifying patterns and making precise predictions about disease progression, treatment responses, and potential adverse events, Notable Labs can tailor treatments to optimize patient outcomes. This personalized approach not only enhances the efficacy of therapies but also minimizes side effects and reduces overall healthcare costs.


Transformative Impact and Investment Appeal

The success of Notable Labs highlights the immense investment potential within the AI healthcare sector. By focusing on precision medicine, Notable Labs addresses significant unmet medical needs, particularly in oncology. Their ability to harness AI for developing targeted therapies has attracted substantial interest from investors, further underscoring the value and promise of AI-driven healthcare solutions.


Investors looking to capitalize on the rise of AI in healthcare can draw valuable insights from Notable Labs' approach. Their focus on integrating AI with precision medicine serves as a model for other startups and emphasizes the importance of data-driven, patient-centric innovation in the healthcare industry.


Future Outlook

As AI continues to reshape the landscape of healthcare, Notable Labs remains a key player to watch. Their pioneering efforts in predictive precision medicine exemplify how AI can revolutionize patient care, offering new hope for effective treatments and better health outcomes. For investors, the ongoing advancements by companies like Notable Labs present exciting opportunities to support groundbreaking innovations that are poised to transform healthcare as we know it.


Conclusion


The transformative potential of AI in healthcare cannot be overstated. From precision medicine and drug discovery to diagnostics, clinical decision support, and patient monitoring, AI is revolutionizing the way healthcare is delivered and experienced. The integration of AI into healthcare systems promises to improve patient outcomes, streamline processes, reduce costs, and accelerate the development of new treatments.


Investing in AI healthcare startups presents significant opportunities, but it also requires informed decision-making. Understanding the regulatory landscape, ensuring data quality and privacy, addressing ethical considerations, and navigating the challenges of adoption, integration, scalability, and commercialization are crucial for success.


Staying updated on the latest developments and trends in AI healthcare is essential for making informed investment decisions. By doing so, investors can capitalize on the immense potential of AI to drive innovation and improvement in healthcare while effectively managing the associated risks and challenges.


As AI continues to advance and reshape the healthcare landscape, the time is ripe for investors 

to engage with this dynamic and promising sector. With a strategic approach and a commitment to responsible investment, the rewards can be substantial, both in terms of financial returns and in contributing to the advancement of healthcare for the benefit of patients worldwide.

A nurse is holding a bottle of pills in her hand.

HealthTech Innovations in Chronic Disease Management

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Discover how HealthTech innovations are transforming chronic disease management. This blog highlights the latest technologies like remote monitoring, AI, and mobile apps, which improve patient care, outcomes, and reduce costs. Explore real-world examples and see how personalized and preventive approaches enhance quality of life for millions.
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Notable Labs Receives FDA Clearance to Proceed Further with Volasertib Phase 2 Study

By Daniel Ferry • July 29, 2024
Notable Labs is a clinical-stage precision oncology company developing new cancer therapies identified by its Predictive Medicine Platform (PMP), today announced progress for the Phase 2 volasertib program following receipt of “Clearance to Proceed” from the FDA and agreement on the dosing plan for its Phase 2 clinical trial. With trial start-up activities launched in the first quarter of this year, Notable intends to initiate enrollment in the Phase 2 study for PMP-enabled development of volasertib for relapsed refractory (R/R) acute myeloid leukemia (AML) in the coming months.
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Notable Labs: Revolutionizing Pediatric Leukemia Treatment

By Syeda Abeer Iqbal • July 29, 2024
Notable Labs is transforming pediatric leukemia treatment with its Predictive Precision Medicines Platform (PPMP), which boasts a 97% predictive precision. This platform rapidly identifies the most effective treatments for individual patients, significantly reducing the time and cost of cancer therapy. Notable Labs focuses on personalized medicine, combining high-throughput screening and machine learning to predict patient responses and tailor treatments accordingly. Their innovative approach has shown promising results in clinical validation studies, offering new hope for improving outcomes in pediatric leukemia.
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Notable Labs' Innovative Approach to Cancer Treatment

By Scott Smith • July 29, 2024
Notable Labs is revolutionizing cancer treatment with its innovative approach to combination therapy, which targets multiple cancer pathways simultaneously. Their Predictive Precision Medicine Platform (PPMP) leverages advanced data analysis and machine learning to identify the most effective drug combinations for individual patients. By using high-throughput screening and bio-simulation, Notable Labs accelerates drug development, reduces costs, and improves clinical trial success rates. This approach promises significant improvements in patient outcomes, particularly for pediatric leukemia, by providing personalized, effective treatments and minimizing side effects.
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Notable Labs Revolutionizing Precision Medicine

By Ayushi Hisaria • July 29, 2024
Notable Labs' cutting-edge diagnostic technology, boasting a 95% prediction accuracy per a Stanford study, is set to revolutionize pediatric leukemia treatment by identifying the most effective therapies. Their mission centers on advancing precision diagnostics to enhance therapy efficacy for young patients. Utilizing high-throughput screening and machine learning, Notable's platform offers tailored treatment plans, significantly improving outcomes for rare and aggressive forms of leukemia. This approach holds promise for personalized medicine, ensuring each child receives optimal, life-saving care.
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How Notable Labs is Revolutionizing Cancer Treatment

By Kathleen Mackay • July 29, 2024
Notable Labs is transforming cancer treatment with its innovative combination therapy approach. By leveraging its Predictive Precision Medicine Platform (PPMP), Notable identifies optimal drug combinations tailored to individual patients. This methodology aims to reduce the development of resistant cancer cells and allows each drug to be used at its best dosage without intolerable side effects. The platform has shown impressive predictive accuracy in clinical trials, particularly for pediatric leukemia. Notable's commitment to precision medicine offers new hope for patients with high unmet medical needs, driving better treatment outcomes and enhancing quality of life.
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Innovative Diagnostics for Pediatric Leukemia: The Notable Labs Advantage

By Fabian Goguta • July 29, 2024
Notable Labs leverages predictive precision medicine to revolutionize cancer treatment, particularly pediatric leukemia. Their Predictive Medicine Platform (PMP) uses advanced technologies like flow cytometry and machine learning to analyze patient-specific cancer cell responses, offering highly accurate predictions for effective treatments. Clinical validation studies have shown a 97% precision in predicting responders. Collaborations with renowned institutions like Texas Children’s Hospital and UCSF further highlight Notable's commitment to improving pediatric oncology outcomes, making personalized, rapid, and effective cancer treatments a reality.
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Precision Medicine in Pediatric Leukemia

By Divya Narasimhan, MSc • July 29, 2024
Precision medicine is revolutionizing pediatric leukemia treatment by offering tailored therapies based on individual genetic profiles. Notable Labs’ predictive precision medicine platform, driven by machine learning, is at the forefront of this transformation. With a 95% accuracy rate, this technology identifies the most effective treatments for rare and aggressive leukemia subtypes, such as pAML and JMML, ensuring better outcomes. This blog explores the advanced technological foundations, life-saving impact, and promising future of Notable Labs' approach in personalized cancer care for children.
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Precision Medicine in Childhood Leukemia

By Dr. David Letsa • July 29, 2024
Notable Labs, founded by Matthew De Silva, emerged from his personal quest to find better treatments for glioblastoma after his father's diagnosis. Partnering with Dr. Thomas Bock, De Silva's vision turned into a biotech startup revolutionizing cancer treatment through its Predictive Precision Medicine Platform (PPMP). The company's leadership team includes industry veterans like Dr. Joseph Wagner, Dr. Glenn Michelson, and Scott McPherson. Headquartered in Foster City, California, Notable Labs collaborates with top hospitals and leverages cutting-edge drug screening technology to deliver personalized cancer therapies, significantly improving patient outcomes.
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The Promise of Combination Therapy

By Dr. Anjaney Kothari • July 29, 2024
Given the complexity of the biological mechanisms that underlie cancer, it is prudent to identify and target more than one Achilles’ heel to attack this multifactorial disease. This can be achieved by employing combination therapy, an approach to cancer treatment that combines multiple therapeutic agents to improve patient outcomes. Notable Labs is at the forefront of revolutionary advancements in combination therapy, one noteworthy example being its long-standing collaboration with Syros Pharmaceuticals. For Syros, Notable developed a highly efficient predictive biological test to screen patients for the clinical trial of a combination therapy against non-acute promyelocytic leukemia (APL) acute myeloid leukemia (AML). Notable Labs’ innovative approach to combination therapy holds immense promise for significantly improving cancer treatment outcomes despite current limitations in commercial pursuit. This blog dives into the science behind the promise of combination therapy, how Notable is contributing toward the evolution of this technology, and why the company’s Board of Directors is excellently positioned to advance this field in the future. Notable Labs' investigation into combination therapy against cancer is backed by decades of scientific research. Combination therapy holds immense promise in the fight against cancer because of its ability to additively or synergistically target two or more cancer pathways. Due to its multi-target approach, combination therapy is also an effective strategy to overcome anti-cancer drug resistance. By collaborating with Syros in 2017, Notable Labs contributed to the phase II clinical trial of a therapy combining the drugs tamibarotene and azacitidine against non-APL AML. For this multi-arm monotherapy and combination therapy trial, Notable developed a blood-based biomarker test to help predict patient response to treatment. Specifically, Notable was involved in conducting ex vivo experiments on peripheral blood mononuclear cells (PBMCs) isolated from the blood samples of non-APL AML patients. Harnessing the power of high-throughput flow cytometry, these experiments revealed that a 72-hour ex vivo exposure of PBMCs to tamibarotene upregulates various markers of myeloid cell differentiation, including the RARα pathway target CD38. Importantly, treatments that can promote myeloid cell differentiation are known to be effective against some subtypes of AML. Therefore, by testing a patient’s blood sample using Notable’s ex vivo approach, it is possible to predict whether the patient is likely to respond well to tamibarotene in monotherapy and combination therapy. Indeed, Notable’s ex vivo assay became one of the strategies Syros adopted when screening patients for enrolment in the phase II clinical trial of tamibarotene (alone as well as in combination with azacitidine). Market Challenges Currently, cancer treatment – and thus, the oncology market as a whole – is faced with a three-pronged challenge. There are millions of non-responders who are not benefiting from chemotherapeutic drugs, thousands of compelling drugs that need to be shelved because of perceived non-response, and hundreds of promising candidate drugs that are struggling to navigate the drug development pipeline. Notable Labs is committed to resolving these challenges through its successfully tested predictive precision medicine pipeline, a significant application of which was demonstrated during the Syros clinical trial.  Notable Labs’ commitment is also one of the reasons why it has decided not to commercialize the combination therapy technology immediately. Notable wants to focus on refining the technology further, expanding its applications, and ensuring that it integrates seamlessly into existing healthcare systems. The company aims to develop more sophisticated prediction models and address regulatory and logistical challenges. This approach will help make Notable's technology accessible to a broader patient population, maximizing the life-saving potential of predictive diagnostics as well as combination therapy. Broader Applications The high-throughput nature of Notable’s ex vivo flow-cytometry-based predictive precision medicine pipeline has helped identify many powerful drug combinations to treat other cancers such as juvenile myelomonocytic leukemia. This pipeline also has great potential for identifying effective drug combinations and guiding patient enrolment in clinical trials of combination therapies for other cancers, including solid tumors. In the case of solid tumors, the Notable pipeline’s blood samples for ex vivo testing could be replaced by biopsy samples, circulating tumor cells, pleural effusion, or ascites, among other sampling options. Founding Story Notable’s CEO, Dr. Thomas Bock (MD, MBA), has ensured that the company is well-positioned to transform combination therapy for cancer. For decades, our CEO has worked on multiple dimensions of oncology, including precision medicine, prevention of inherited cancers, and cancer vaccines. At Notable, Dr. Bock combines his medical and business training to accomplish the company’s mission of dramatically improving patient outcomes and the success, speed, and cost of developing new medicines. Strength of the Board of Directors Notable’s esteemed Board of Directors boasts of more than 150 years of combined professional experience in premier life science companies. Our board members excel in medicine (Dr. Bock), pharmacology (Mr. Tuomo Pätsi), business (Dr. Bock), finance (Mr. Michael Rice, Mr. Peter Feinberg, Mr. Thomas Graney), media communications (Ms. Michele Galen), and law (Mr. Thomas Dubin), fostering a vibrant and eclectic environment for Notable to thrive. This diverse team of bona fide giants from the pharmaceutical and biotechnology sectors is set to efficiently guide Notable Labs on its path to success in combination therapy and beyond. Conclusion Combination therapy for cancer, aided by predictive methods, could significantly improve patient outcomes while also broadening the population of patients who can benefit from a given drug combination. Leading the charge in this direction, Notable Labs’ predictive precision medicine pipeline boasts of massive potential to not only identify effective drug combinations for combination therapy, but also guide patient enrolment in clinical trials through predictive screening. Moving ahead, Notable’s technology is set to transform the treatment of multiple types of cancer by ensuring positive patient outcomes of personalized combination therapies in shorter times and at lower costs. Additional Data Points AML statistics (American Cancer Society): estimated 20,800 new diagnoses and 11, 220 deaths in the US in 2024. Success stories and collaborations in the field of combination therapy: Notable’s collaboration with Syros to develop a predictive test for patient response to monotherapy and combination therapy. Potential impact on cancer treatment outcomes: Combination therapy, aided by predictive methods, has the potential to enhance patient outcome while also broadening the population of patients who can benefit from a given combination of drugs. Future directions for research and development: AML treatment strategies must pivot towards personalized/precision medicine because of overwhelming heterogeneity of the disease. References Combination therapy in combating cancer . (2017). Oncotarget Notable’s collaboration with Syros . (2017). Notable Labs + Syros Differentiation therapy for myeloid malignancies: beyond cytotoxicity . (2021). Blood Cancer Journal New strategies to treat AML: novel insights into AML survival pathways and combination therapies . (2021). Leukemia Precision oncology using ex vivo technology: a step towards individualised cancer care? . (2022). Expert Reviews in Molecular Medicine Targeting RARA overexpression with tamibarotene, a potent and selective RARα agonist, is a novel approach in AML . (2023). Blood Advances Notable Labs’ JMML data . (2023). Notable Labs Key statistics for acute myeloid leukemia (AML) . (2024). American Cancer Society About the Author Dr. Anjaney Kothari is a scientific writer and researcher with a decade of experience in biomedical research. He completed his Ph.D. in Biomedical Engineering from Virginia Tech (USA) in 2019, developing and characterizing holistic in vitro and ex vivo models of hepatic and gastrointestinal toxicity. He has since been working as a freelance writer and researcher for companies operating in diverse niches, including biotechnology and biopharmaceuticals. Disclaimer The content of these blogs reflects the research and opinions of the individual authors and does not necessarily represent the views or positions of Notable Labs or its affiliates. The information provided is for educational and informational purposes only and should not be construed as medical, legal, or financial advice. Notable Labs makes no representations as to the accuracy, completeness, or validity of any information in these blogs and will not be liable for any errors, omissions, or any losses, injuries, or damages arising from their use. These blogs may reference third-party research, studies, or resources. Notable Labs does not endorse or assume responsibility for the content or practices of these third parties. Any reliance on the information provided is at the reader's own risk. For biotechnology and pharmaceutical content, note that ongoing research and clinical trials may change the context and results discussed. Always refer to the latest research and guidelines from reputable sources.
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