The future of infectious disease diagnostics starts at Day Zero

Jong Lee

Problem Tech Solves


Tech Brief

Growing antibiotic resistance, named a global public health threat by the WHO and CDC, has fundamentally changed clinical practice. The difficulty of diagnosing drug resistance without multi-day culture results, requires physicians to treat patients with life threatening infections with ever more powerful, broad spectrum antibiotics based on imprecise assessment of clinical symptoms and best guesses about which strain is the cause of infection. This “empiric therapy” strategy is ineffective 20-30% of the time due to antibiotic resistance, but in an additional 40-50% of cases, prolonged exposure to empiric therapy results in increased mortality and morbidity due to the antibiotics themselves. Our first indication, Bloodstream infections (BSI) and sepsis, is a category where knowing the antibiotic susceptibility (AST) profile quickly is particularly critical. Sepsis is the leading cause of death and costs in US hospitals, responsible for 1 in 3 deaths and $62B in annual care costs for CMS alone. In sepsis, time is of the essence because every hour of delayed appropriate therapy is associated with an increased risk of death. But in 40% of sepsis cases, blood cultures fail to grow. When cultures do grow, it can take 2-5 days to return a result that includes both ID and AST. So sepsis patients are subjected to prolonged empiric therapy that is ineffective 20-30% of the time, and even when effective, increases mortality risk by 20% and results in complications such as organ dysfunction, c. diff colonization, and long-term disability. DZD enables targeted treatment on Day Zero of hospital admission.

Tech Differentiators

Sepsis has attracted many efforts to accelerate diagnostics by bypassing standard of care culture based workflows, but culture remains a core component of AST profiling. All current and future diagnostics we are aware of face a trade-off between speed and comprehensiveness as a result. Sepsis host-response (mRNA) diagnostics have the ability to confirm if a patient has sepsis within an hour. This can be helpful for confirming an infection, but provide none of the species ID or AST profiling required to guide treatment choices. Many molecular diagnostics target faster species ID, but most are dependent on culture growth for their sample, requiring hours to days for culture growth and uninformative when culture fails to grow. Molecular diagnostics that attempt to work directly from blood use biomarker approaches that limit them to a small panel of organisms (e.g., 5 species for the only FDA cleared system) and none of them have the capability to perform comprehensive AST. In an era of growing antibiotic resistance, both ID and AST are necessary to solve sepsis. Diagnostics that are capable of both universally require time-consuming culture as an input -- until now. DZD’s ultra-high enrichment platform enables rapid whole genome sequencing directly from blood without the need for culture, enabling comprehensive Species ID and AST in hours using our advanced ML algorithms. In addition, the stream of sequencing data enables a range of data services for hospital epidemiology and lab operations that are currently unimagined in infectious disease diagnostics.


We have entered into strategic partnerships with academic medical centers (e.g., MGH, UCI, UAMS) to build out MicrohmDB, one of the most comprehensive microbiology datasets in the world. MicrohmDB is a unique, growing dataset that consists of the whole genome sequences of clinically relevant multi-drug resistant organisms paired with their phenotypic AST results as well as other meta-data. While there are many pathogen genomic datasets, there are only a few in the world that combine high quality whole genome sequences with paired AST results at scale. The database contains 45,000 samples (and growing) primarily from academic hospital collaborators and customers, antibiotic resistance focused biobanks, and published studies. MicrohmDB is a proprietary asset that enables us to use machine learning rather than hand crafted resistance gene models or biomarkers to predict AST. In addition, we are in dialogue with sequencing companies to more fully integrate a commercially available sequencing platform into our solution. Particularly, we are engaged in a collaboration with Oxford Nanopore Technologies (ONT) to optimize our diagnostics for nanopore sequencing data. This collaboration enables us to offer a very rapid sequencing solution, with a sample-to-answer solution in less than 8 hours. Our dialogue is not limited to ONT, as we are designing our system to be platform-agnostic. With the rapid advancements in sequencing capability and accuracy, and exponential decrease in cost per GB of data, we expect to see broad adoption of sequencing platforms at the microbiology labs in the next 3-5 years.

Why Us

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