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Award details
- Awarding agency
- Department of Health and Human Services
- Branch
- National Institutes of Health
- Recipient company
- AKONNI BIOSYSTEMS INC
- Proposal award date
- 2021-04-21
- Contract end date
- 2022-03-31
- UEI
- PGLAYC4UKDL7
- Solicitation number
- PA20-260
- Topic code
- NIAID
Research abstract
ABSTRACT
Chronic lung disease due to non-tuberculous mycobacteria (NTM-LD) is a silent and emerging epidemic in the
U.S. and many parts of the world.1-3 The incidence and prevalence of NTM-LD is increasing yearly and now
surpasses that of tuberculosis (TB) in the U.S.4 Two of the greatest known risk factors for NTM-LD are chronic
obstructive pulmonary disease (COPD) and pre-existing bronchiectasis.3 In the U.S., approximately 12 million
individuals have COPD, and it is the third leading cause of death in the U.S.5 In addition, the co-occurrence of
bronchiectasis in patients with known COPD is estimated to be up to ~70%.6 Like TB, the requirement for
prolonged combination drug therapy is a central tenet of NTM treatment. Consequently, it is essential that
several drugs be administered concurrently to maximize sterilizing conditions. Unlike TB for which there are
now rapid molecular tests for detection of infection and antimicrobial resistance and recently approved new
drugs, diseases caused by NTM are a major concern for multiple reasons, foremost being their intrinsic
resistance to most existing antimicrobials in the absence of a rapid diagnostic test for antimicrobial
susceptibility or even NTM infection.7,8 Diagnosis of NTM-LD is complicated by the fact that clinical
manifestations and radiographic findings for TB and NTM-LD are virtually indistinguishable. Thus, it is
important when diagnosing NTM-LD to “rule out” TB even in regions of lower prevalence, such as the U.S.
because of its infectious and deadly nature. To differentiate TB from NTM-LD, clinicians must rely on a
combination of mycobacteriology and molecular tests to identify the etiological agent of the disease, which for
NTM includes 13 clinically-relevant species/sub-species that must be distinguished from approximately 180
other NTM species that are rarely pathogenic. In addition to the challenge of accurately speciating
mycobacteria, there is also the need to detect antibiotic resistance. Hence, the algorithm for mycobacterial
diagnostic testing is complex, requiring varied testing methodologies, which are either slow to respond
(culture), insensitive (smears), or insufficiently comprehensive (molecular).
Molecular detection of respiratory mycobacterial infections and resistance to antibiotics is challenging. The
specimen type (sputum) is viscous and highly heterogeneous; the mycobacteria bacilli are difficult to lyse; the
number of species is considerable; and the polymorphisms that confer drug resistance are numerous. To
address these challenges, we propose to automate and integrate the following into a one user-step test:
chaotic mixing of glass beads using a rotating magnetic disc to homogenize sputum and lyse bacilli, a porous
disc in a pipette tip to purify and concentrate nucleic acid, and a Lab-on-a-Film test to speciate and detect
polymorphisms that confer drug resistance. For Phase 1, we propose to develop a Lab-on-a-Film test that has
the required sensitivity to rule in/out TB, diagnose NTM-LD, and detect drug resistance when Mycobacterium
tuberculosis (MTB) and/or clinically important NTM are present.PROJECT NARRATIVE
Mycobacteria that cause respiratory infections, such as tuberculosis and non-tuberculous mycobacteria lung
disease, require many months and tens of thousands of dollars for treatment. The diagnostic algorithm to
identify (or rule out) the organism that is the cause of the infection (and detect drug resistance), is complex
because of a lack of a comprehensive, sensitive and rapid test. We propose to develop a one-step molecular
test that identifies (or rules out) clinically-relevant mycobacteria species and sub-species that cause respiratory
infections and detects genetic mutations that confer drug-resistance.
PHS 398/2590 (Rev. 09/04) Page Continuation Format Page
Technology keywords
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Research funding history
24 SBIR/STTR records share this UEI (2006–2022). Reported award amounts total $23,362,860. Reported amounts are not federal obligations.
Phases: Phase I, Phase II
Agencies: Department of Health and Human Services, National Science Foundation, Department of Energy
Agency intelligence
Department of Health and Human Services
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