LungLife AI, Inc.
(the "Company" or
"LungLife")
LungLB® analytical validation accepted
for publication in peer-reviewed journal
LungLife AI (AIM: LLAI), a developer of
clinical diagnostic solutions for lung cancer, announces the acceptance for publication of data
confirming analytical performance of the Company's
blood-based LungLB® test in the journal BMC Pulmonary Medicine.
Springer Nature's BMC Pulmonary Medicine is
a globally recognised peer-reviewed medical
journal with articles covering all aspects of the prevention,
diagnosis and management of pulmonary and associated disorders. The
full publication, entitled "Analytical Validation of the LungLB®
test: a 4-color fluorescence in-situ hybridization assay for the
evaluation of indeterminate pulmonary nodules" can be accessed
here
https://doi.org/10.1186/s12890-024-03280-7.
Publication in scientific journals is a crucial
step in the commercialisation of LungLB® as the peer-review process
supports the verification of the reliability and credibility of the
research, building trust and confidence within the scientific and
medical communities.
This publication adds to the growing
body of evidence supporting LungLB®, including clinical validity and
health economics data published in 2023, and is a key element in
obtaining coverage for Medicare reimbursement, opening the test up
for Medicare patients and increasing the likelihood of the test
being adopted by centres.
As a reminder, the analytic
validation experiments were done in accordance with the globally
recognised Clinical and Laboratory Standards Institute
(CLSI) guidelines, of
which the US FDA recognises over 100
CLSI consensus standards, to evaluate
sample stability and assay
reproducibility under a variety of clinical and laboratory
conditions. Collectively, the results demonstrate the
LungLB® test worked
consistently well, no matter which lab technician was running it,
when it was done, or which batch of materials was used. This is
important as it shows that the test is reliable and
well-designed.
Eric Vail MD,
Director of Molecular Pathology at Cedars Sinai Medical Center,
Laboratory Director of LungLife AI, and co-author on the study
commented:
"I am
delighted that the analytic validation has been published in a highly
regarded journal. Novel diagnostic tests must undergo
rigorous analytical testing prior to clinical use to demonstrate
accuracy and reliability in routine laboratory settings. The data show to the scientific and medical community that the
LungLB® test is robust and
suitable to everyday clinical use."
Paul Pagano, Chief Executive Officer of LungLife,
added:
"Having the results of our study peer-reviewed
and published is a significant milestone,
validating the scientific rigour of our research. This achievement
is a key part of our commercialisation strategy to build a bank of
evidence of the efficacy and utility of our testing. It underscores
both the credibility of our LungLB®
test and
provides greater confidence in its use, and importantly, by raising
awareness about the test it supports our commitment to expanding
access for those who need it most."
For further
information please contact:
LungLife AI,
Inc.
|
www.lunglifeai.com
|
Paul Pagano, CEO
|
via investors@lunglifeai.com
|
David Anderson, CFO
|
|
|
|
Investec Bank plc (Nominated Adviser &
Broker)
|
Tel: +44 (0)20 7597
5970
|
Virginia Bull / Lydia Zychowska / Sara
Wallace
|
|
|
|
Goodbody
(Joint Broker)
|
Tel: +44 (0) 20 3841
6202
|
Tom Nicholson / Cameron Duncan
|
|
|
|
About LungLife
LungLife AI is a developer of
clinical diagnostic solutions designed to make a significant impact
in the early detection of lung cancer, the deadliest cancer
globally. Using a minimally invasive blood draw, the
Company's LungLB® test is designed to deliver additional
information to clinicians who are evaluating indeterminate lung
nodules. For more information visit
www.lunglifeai.com
Our Purpose is to be a driving force in the
early detection to lung cancer. And our Vision is to invert
the 20:80 ratio such that in years to come at least 80% of lung
cancer is detected early.