iNDX.Ai is a Silicon Valley based company that has developed a broad-capability analytical platform that could be a panacea for overcoming some of the most common, time-consuming, and costly clinical research problems Defining the problem Clinical Research is a highly challenging, costly, and risky endeavor. From its pre-clinical inception to the actual market launch, a drug requires – on average of 7-12 years of intense pre-clinical and clinical research, an investment of about ~ $2.6 billion, and successful navigation through several regulatory gauntlets. The stats are even more dismal for Oncology. According to a collaborative study by Biotechnology Innovation Organization (BIO), Biomedracker, and Amplion which reports the approval rates for drugs across 14 major disease areas, the likelihood of approval (LOA) for Oncology drugs is an abysmal 5.1%. The success rates for solid tumors are even worse, with LOAs struggling to reach half this value. Clinical research pundits suggest that the main reasons for such a high failure rate […]

Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshalling and permitting errors of omission and commission because dataset members are not explicitly specified. We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for […]

Though the cryptocurrency side of blockchain technology has dominated news headlines in the last year, the technology has real promise in biotechnology and the life sciences. A recent survey (September 2018) by the Pistoia Alliance found that 60% of professionals in the pharmaceutical and life sciences industries were using or experimenting with blockchain technologies in 2018, as compared with 22% in 2017. Blockchain technology provides three key capabilities1 that are missing but required for the life sciences sector to evolve to the next level: Ultra-secure and immutable storage of information Decentralization and increased transparency of transactions Incentivization of key stakeholders Key use cases for blockchain in biotechnology include: Battling counterfeit drugs Enhancing virtual clinical trials Efficient 3D printing of drugs These issues are ordered according to the likely adoption timeline for the use case. Battling counterfeit drugs Pharmaceutical companies spend billions of dollars and go through an arduous process to produce […]

Biopharma R&D has changed the face of disease management over the years. Yet many in the field admit that clinical development has fallen behind in adopting digital technologies, which have the potential to change how research organizations can engage with patients, innovate in patient care, and execute processes to drive efficiencies. While digital transformation is a complex, resource-intensive, and lengthy undertaking, the rewards are well worth the effort. Executive summary IN the last decade, biopharma companies successfully brought to market many breakthrough treatments that have transformed deadly diseases into manageable chronic conditions, raised the standards of medical care, and helped improve the quality of patients’ lives. However, many in biopharma R&D admit that the current high-risk, high-cost R&D model is unsustainable. A number of clinical trial activities still use the same processes as in the 1990s, and the clinical development enterprise has largely failed to keep pace with the ever-growing amounts […]

Last year, Beth Shia got the worst news a breast cancer survivor can get. Her disease was back. For a third time. Despite two sessions of chemotherapy and radiation. “I knew it wasn’t good … I know what metastatic breast cancer means,” Shia said later. It was time, she thought, to put her affairs in order and focus on quality of life, rather than fighting the disease. But Shia’s doctor, Dr. Jacqueline Vuky, is an oncologist with the OHSU Knight-Legacy Health Cancer Collaborative. She enrolled Shia in their new system of clinical trials, called “Serial Measurements of Molecular and Architectural Response To Therapy” or SMMART. This is not just another clinical trial. When Nike co-founder Phil Knight pledged half a billion dollars to the OHSU Knight Cancer Institute, he asked researchers to throw out the old way of doing things in favor of something completely new. Rather than trying to find the next blockbuster drug, researchers focus on trying to […]

Cancer therapies that activate the immune system have shown remarkable results for many patients. Immune checkpoint inhibitors (ICIs) work by interfering with the mechanism that cancer cells use to evade the host’s immune response. Drugs that block cytotoxic T lymphocyte associated protein 4 (CTLA-4), programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1) activate the cancer-killing T cells that had been suppressed. ICIs can shrink tumours and improve survival rates, even for patients for whom other cancer therapies have failed. Only 20-40% of patients respond to immunotherapy1 and, because these drugs can activate a broad range of immune cells, they can sometimes trigger severe auto-immune reactions. If clinicians can predict who will be a non-responder, they will save treatment costs and spare patients from side-effects. Next-generation sequencing (NGS) technologies are starting to reveal the hallmarks of treatment response. Assays that measure the genetic profile of tumours and the […]

Prime Minister Theresa May continues to push for growth in the United Kingdom’s pharma sector. Today she called for expanding the role of artificial intelligence in the diagnosis process for cancer. Using artificial intelligence (AI) as part of the diagnostic process can reduce potential fatalities from cancer by 10 percent, May said, according to the Financial Times. May is looking to establish something of a new industry around the use of AI in healthcare, the publication added. According to early transcripts of May’s speech, the prime minister decried the late diagnoses of cancers that are treatable. She called them “one of the biggest causes of avoidable deaths.” Additionally, May said the use of “smart technologies to analyze great quantities of data quickly and with a higher degree of accuracy than is possible by human beings opens up a whole new field of medical research and gives us a new weapon in our armory in the […]

GlaxoSmithKline (GSK)’s recently announced $4.2 billion partnership to co-develop and co-commercialize Merck KGaA, Darmstadt’s cancer immunotherapy candidate M7824 was a reminder of just how much biopharma giants are willing to invest in a therapeutic area that has generated sometimes-dazzling results. How high is up for immuno-oncology? Numerous firms have ventured their best projections for the size of the market over the next few years. Transparency Market Research estimates that cancer immunotherapy will grow from $37.5 billion in 2015 to $124.88 billion by 2024, a compound annual growth rate (CAGR) of 14.5%. Other forecasts put the size of the potential future immuno-oncology market by 2024 even higher: from $128.3 billion (KBV Research), to $152.829 billion (Meticulous Research), to $173 billion (Market Research Engine). While the size of the future market remains unknown, what is certain is that immuno-oncology is growing, and that its lead products enjoy blockbuster ($1 billion-plus/year in sales) status several […]