UNITED
STATES
SECURITIES
AND EXCHANGE COMMISSION
Washington,
D.C. 20549
FORM
10-K
(Mark
One)
☒
ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
For
the fiscal year ended December 31, 2025
OR
☐
TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934
Lantern
Pharma Inc.
(Exact
name of registrant as specified in its charter)
1920 McKinney Avenue, 7th Floor Dallas, Texas 75201
(Address of Principal Executive Offices) (Zip Code)
(972)277-1136
(Registrant’s
telephone number, including area code)
Securities
registered pursuant to Section 12(b) of the Act: Common Stock
Title of each class Trading Symbol Name of each exchange on which registered
Common Stock, $0.0001 par value LTRN The Nasdaq Stock Market
Indicate
by check mark if the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. Yes ☐ No ☒
Indicate
by check mark if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No ☒
Indicate
by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange
Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2)
has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐
Indicate
by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule
405 of Regulation S-T (§232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant
was required to submit such files). Yes ☒ No ☐
Indicate
by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting
company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,”
“smaller reporting company,” and “emerging growth company” in Rule 12b-2 of the Exchange Act.
Large accelerated filer ☐ Accelerated filer ☐
Non-accelerated filer ☒ Smaller reporting company ☒
Emerging growth company ☐
If
an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying
with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐
Indicate
by check mark whether the registrant has filed a report on and attestation to its management’s assessment of the effectiveness
of its internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered
public accounting firm that prepared or issued its audit report. ☐
If
securities are registered pursuant to Section 12(b) of the Act, indicate by check mark whether the financial statements of the registrant
included in the filing reflect the correction of an error to previously issued financial statements. ☐
Indicate
by check mark whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation
received by any of the registrant’s executive officers during the relevant recovery period pursuant to §240.10D-1(b). ☐
Indicate
by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Exchange Act). Yes ☐ No ☒
State
the aggregate market value of the voting and non-voting common equity held by non-affiliates computed by reference to the price at which
the common equity was last sold, or the average bid and asked price of such common equity, as of the last business day of the registrant’s
most recently completed second fiscal quarter: $33,935,133.
As
of March 13, 2026, the registrant had 11,254,697 shares of common stock, $0.0001 par value per share outstanding.
DOCUMENTS
INCORPORATED BY REFERENCE
Portions
of the registrant’s definitive proxy statement for the registrant’s 2026 Annual Meeting of Stockholders to be filed pursuant
to Regulation 14A within 120 days of the registrant’s year ended December 31, 2025 are incorporated herein by reference into Part
III of this Annual Report on Form 10-K.
Table
of Contents
Page
Forward Looking Statements ii
PART I.
Item 1. Business 1
Item 1A. Risk Factors 58
Item 1B. Unresolved Staff Comments. 104
Item 1C. Cybersecurity 104
Item 2. Properties. 104
Item 3. Legal Proceedings. 104
Item 4. Mine Safety Disclosures. 104
PART II
Item 6. Reserved. 106
Item 7A. Quantitative and Qualitative Disclosures About Market Risk. 114
Item 8. Financial Statements and Supplementary Data. F-1
Item 9A. Controls and Procedures. 115
Item 9B. Other Information. 116
PART III
Item 10. Directors, Executive Officers and Corporate Governance. 116
Item 11. Executive Compensation. 116
Item 14. Principal Accountant Fees and Services. 116
PART IV
Item 15. Exhibit and Financial Statement Schedules. 117
i
SPECIAL
NOTE CONCERNING FORWARD-LOOKING STATEMENTS
This
Annual Report on Form 10-K contains forward-looking statements that involve substantial risks and uncertainties. We make such forward-looking
statements pursuant to the safe harbor provisions of the U.S. Private Securities Litigation Reform Act, Section 21E of the Securities
Exchange Act of 1934, as amended, and other federal securities laws. All statements, other than statements of historical fact, contained
in this Annual Report on Form 10-K, including statements regarding our strategy, future preclinical study activities, future clinical
trial activities, future research activities, future financial position, projected costs, prospects, plans and objectives of management,
are forward-looking statements. The words “anticipate,” “believe,” “contemplate,” “could,”
“estimate,” “expect,” “intend,” “seek,” “may,” “might,” “plan,”
“potential,” “predict,” “project,” “target,” “model,” “objective,”
“aim,” “upcoming,” “should,” ‘will,” “would,” or the negative of these words
or other similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain
these words. Forward-looking statements reflect our current views with respect to future events and are based on assumptions and subject
to risks and uncertainties.
The
forward-looking statements in this Annual Report on Form 10-K include, among other things, statements relating to:
● our strategic plans to advance the development of any of our drug candidates;
● our expectations related to future expenses and expenditures;
ii
We
may not actually achieve the plans, intentions, or expectations disclosed in our forward-looking statements, and you should not place
undue reliance on our forward-looking statements. Actual results or events could differ materially from the plans, intentions, and expectations
disclosed in the forward-looking statements we make. Factors that may cause actual results or events to differ materially from current
plans, intentions, and expectations include, among other things:
These
factors could cause actual results or events to differ materially from the forward-statements that we make. Furthermore, we operate in
a competitive and rapidly changing environment. New risks and uncertainties emerge from time to time, and it is not possible for us to
predict all risks and uncertainties that could have an impact on the forward-looking statements contained in this Annual Report on Form
10-K.
You
should read this Annual Report on Form 10-K and the documents that we file with the Securities and Exchange Commission, or the SEC, with
the understanding that our actual future results may be materially different from what we expect. These forward-looking statements are
based on management’s current expectations. These statements are neither promises nor guarantees, but involve known and unknown
risks, uncertainties and other important factors that may cause our actual results, performance or achievements to be materially different
from any future results, performance or achievements expressed or implied by the forward-looking statements. Factors that may cause actual
results or events to differ materially from current plans, intentions, and expectations include, among other things, those listed under
Part I, Item 1A. “Risk Factors,” Part II, Item 7. “Management’s Discussion and Analysis of Financial Condition
and Results of Operations” and elsewhere in this Annual Report on Form 10-K. Given these uncertainties, you should not rely on
these forward-looking statements as predictions of future events. The forward-looking statements contained in this Annual Report on Form
10-K are made as of the date of this Annual Report on Form 10-K, and we do not assume any obligation to update any forward-looking statements,
whether as a result of new information, future events or otherwise, except as required by applicable law.
In
addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These
statements are based upon information available to us as of the date of this Annual Report on Form 10-K, and while we believe such information
forms a reasonable basis for such statements, such information may be limited or incomplete. Our statements should not be read to indicate
that we have conducted an exhaustive inquiry into, or review of, all potentially available relevant information. These statements are
inherently uncertain and investors are cautioned not to unduly rely upon these statements.
Unless
the context requires otherwise, references to the “Company,” “Lantern,” “we,” “us,” and
“our” in this Annual Report on Form 10-K refer to Lantern Pharma Inc., a Delaware corporation, and, where appropriate, its
wholly-owned subsidiaries.
iii
RISK
FACTOR SUMMARY
Our
business is subject to numerous risks and uncertainties, including those described in Part I, Item 1A. “Risk Factors” in
this Annual Report on Form 10-K. These risks include, but are not limited to the following:
iv
● We may be at risk of securities class action litigation.
v
PART
I
Item
1. Business
Overview
We
are an artificial intelligence (A.I.) focused company dedicated to developing cancer therapies and transforming the cost, pace, and timeline
of oncology drug discovery and development. Our development portfolio includes three clinical stage oncology focused product candidates
and consists of small molecules that others have tried, but failed, to develop into an approved commercialized drug, as well as new compounds
that we are developing with the assistance of our proprietary A.I. platform and our biomarker driven approach. Our A.I. platform, known
as RADR®, currently includes more than 200 billion data points, and uses big data analytics (combining molecular data,
drug efficacy data, data from historical studies, data from scientific literature, phenotypic data from trials and publications, and
mechanistic pathway data) and machine learning to rapidly uncover biologically relevant genomic signatures correlated to drug response,
and then identify the cancer patients that we believe may benefit most from our compounds. This data-driven, genomically-targeted and
biomarker-driven approach allows us to pursue a transformational drug development strategy that identifies, rescues or develops, and
advances potential small molecule drug candidates at what we believe is a fraction of the time and cost associated with traditional cancer
drug development. On average, our newly developed drug programs have been advanced from initial A.I. insights to first-in-human clinical
trials in 2-3 years and at approximately $1.0-$2.5 million per program.
We
have active clinical programs for our three lead small
molecule drug candidates: LP-300, LP-184, and LP-284. These programs are focused on multiple important
cancer indications, including both solid tumors and blood cancers. We have established a wholly-owned subsidiary, Starlight Therapeutics,
to focus exclusively on the clinical development of our promising opportunities for central nervous system (“CNS”)
and brain cancers, many of which have no effective treatment options. We are also advancing an antibody-drug conjugate (“ADC”)
program focused on developing highly specific ADCs with highly potent drug-payloads.
In January 2026, we introduced withZeta.ai — a generative AI platform purpose-built to empower researchers
and clinicians to accelerate rare cancer research and drug development, dramatically improve research quality, and reduce R&D costs.
withZeta’s multi-agentic architecture combines intelligent orchestration using a combination of proprietary knowledge bases and publicly
available data with autonomous task completion to deliver a true “co-scientist” experience — one that brings the collective
insight of thousands of domain experts, millions of publications, and billions of data points to address some of oncology’s most difficult
challenges and disease subtypes.
In
2025, the FDA cleared two new Phase 1b/2 investigational new drug (IND) applications for LP-184, further expanding our clinical pipeline
opportunities. The first planned LP-184 Phase 1b/2 trial is positioned to evaluate LP-184 in recurrent triple negative breast cancer
(TNBC) patients as both a monotherapy and in combination with the PARP inhibitor olaparib. The second planned LP-184 Phase 1b/2 trial
is positioned to evaluate LP-184 in a biomarker-defined population of non-small cell lung cancer (NSCLC) patients harboring KEAP1 and/or
STK11 mutations with low PD-L1 expression, in combination with the immune checkpoint inhibitors nivolumab and ipilimumab — a population
with high unmet clinical need and a market opportunity estimated to exceed $2 billion annually. Additionally, LP-184 has received FDA
Fast Track Designations for GBM and TNBC, as well as multiple Orphan Drug and Rare Pediatric Disease Designations across various solid
tumor indications.
Our
strategy is to both develop new drug candidates using our RADR® platform and other machine learning driven methodologies,
and to pursue the development of drug candidates that have undergone previous clinical trial testing or that may have been halted in
development or deprioritized because of insufficient clinical trial efficacy or for strategic reasons by the owner or development team
responsible for the compound. Importantly, these historical drug candidates appear to have been well-tolerated in many instances, and
often have considerable data from previous toxicity, tolerability and ADME studies that have been completed. Our dual approach to both
develop de-novo, biomarker-guided drug candidates and “rescue” historical drug candidates by leveraging A.I., recent advances
in genomics, computational biology and cloud computing is emblematic of a new era in drug development that is being driven by data-intensive
approaches meant to de-risk development and accelerate the clinical trial process. In this context, we are working to create a diverse
portfolio of oncology drug candidates for further development towards regulatory and marketing approval with the objective of establishing
a leading A.I. driven, methodology for treating the right patient with the right oncology therapy.
A
key component of our strategy is to target specific cancer patient populations and treatment indications identified by leveraging our
RADR® platform, a proprietary A.I. enabled engine created and owned by us. Our RADR® platform has grown
to encompass more than 200 billion oncology-focused data points across proprietary, collaborative, and public sources, and employs a
library of 200+ advanced machine learning algorithms. During 2025, we have continued to expand the RADR® platform with
several significant new modules, including: (i) an AI-powered ADC development module that identifies targets, payloads, and tumor selectivity
using a multiomic approach; (ii) a combination regimen module trained on 221 clinical trials to predict the activity and efficacy of
DNA-damaging agent and DNA repair inhibitor combinations; and (iii) a blood-brain barrier (BBB) permeability prediction model that can
process up to 100,000 molecules per hour, for which a PCT patent application has been published with a favorable search report indicating
no significant prior art. Lantern’s BBB prediction algorithms currently hold five of the top ten positions on the Therapeutic Data
Commons (TDC) Leaderboard. We plan to commercially release select RADR® AI modules to the broader research and drug development
community to foster collaborative, open-source innovation in oncology.
Scientific
literature offers a definition for “drug rescue” as research involving abandoned small molecules and biologics that have
not been approved by the U.S. Food and Drug Administration (“FDA”). These rescued molecular compounds are often abandoned
by pharmaceutical companies in the drug discovery or preclinical testing phase, typically because they do not prove effective for the
specific use for which they were developed. Some of these compounds may be useful in treating other diseases for which they have not
been tested. See, Hemphill, Thomas A., “The NIH Promotes Drug Repurposing and Rescue,” Research Technology Management,
v. 5, no. 5, pp. 6-8 (2012). Our use of the term “rescue”, “drug rescue”, or “drug rescuing” refers
to, “...a system of developing new uses for chemical and biological entities that previously were investigated in clinical
studies but not further developed or submitted for regulatory approval, or had to be removed from the market for safety reasons.”,
which is a definition we believe is recognized in the drug discovery, drug development and pharmaceutical and biotechnology industries.
See, Naylor, S. and Schonfeld J., “Therapeutic Drug Repurposing, Repositioning and Rescue,” DDW (Drug Discovery World)
Winter 2014, and Mucke, HAM, A New Journal for the Drug Repurposing Community. Drug Repurposing, Rescue & Repositioning 1, 3-4 (2014).
The use of the term “drug rescue,” “rescuing,” or words of similar meaning in this report should not be construed
to mean that our RADR® platform has resolved all issues of safety and/or efficacy for any of our drug candidates. Issues
of safety and efficacy for any drug candidate may only be determined by the U.S. FDA or other applicable regulatory authorities in jurisdictions
outside the United States.
Our
current portfolio consists of three lead drug candidates that are in clinical phases (known as LP-300, LP-184 and LP-284) and an Antibody
Drug Conjugate (ADC) program that is in preclinical research optimization. In January 2023, we formed a wholly owned subsidiary, Starlight
Therapeutics Inc. (“Starlight”), to develop drug candidate LP-184’s central nervous system (CNS) and brain cancer indications
– including glioblastoma (GBM), brain metastases (brain mets.), and several rare pediatric CNS cancers. Following the formation
of Starlight, we may also refer to the molecule LP-184, as it is developed in CNS indications, as “STAR-001”. All of these
drug candidates and our ADC program are leveraging precision oncology, A.I. and genomic driven approaches to accelerate and direct development
efforts.
We
are conducting a targeted phase 2 trial (the HarmonicTM trial) for LP-300 in never smoking
patients with advanced non-small cell lung cancer (“NSCLC”) in combination with chemotherapy, under an existing investigational
new drug application. Our candidate LP-184 has shown promising in-vitro and in
vivoanticancer activity in multiple solid tumor indications (including pancreatic, lung, bladder,
glioblastoma and triple negative breast cancer), and enrollment has now been completed in a Phase 1a clinical trial for LP-184. Based
on the results and insights from the LP-184 Phase 1a clinical trial, we are advancing and optimizing development plans for multiple future
LP-184 clinical studies. Our candidate LP-284 has shown promising in-vitro and
in vivoanticancer activity in multiple hematological cancers, which are distinct
from the indications targeted by LP-184. LP-284 is advancing in a Phase 1A clinical trial.
Our
ADC program has also continued to advance. During 2024 and in 2025, we continued to apply our RADR® A.I. platform to advance
and refine an A.I. powered module focused on improving the precision, cost and timelines of ADC development for cancer. In
2023, we entered into a research collaboration with Bielefeld University in Germany focused on development of ADCs utilizing cryptophycin
as the ADC drug-payload. Cryptophycins are promising antitumor molecules that have demonstrated potency at ultra-low, picomolar, concentrations.
In a broad range of preclinical studies, the cryptophycin-ADC synthesized as part of the Bielefeld
collaboration demonstrated promising picomolar level potency and anti-tumor activity in multiple solid tumor cell lines, including breast,
bladder, colorectal, gastric, pancreatic and ovarian.
In
addition to our lead drug candidates and ADC program, we also have an additional drug candidate, LP-100, that we believe has potential
for future development in combination with the class of anticancer agents known as PARP inhibitors (PARPi). For LP-100, as well as our
lead drug candidate LP-300, we have leveraged data from prior preclinical studies and clinical trials, along with insights generated
from our A.I. platform, to target the types of tumors and patient groups we believe will be most responsive to the drug. Both LP-100
and LP-300 showed promise in important patient subgroups, but failed pivotal Phase 3 trials when the overall results did not meet the
predefined clinical endpoints. We believe that this was due to a lack of biomarker-driven patient stratification.
LP-300
has been studied in multiple randomized, controlled, multi-center non-small cell lung cancer, or NSCLC, trials that included administration
of either paclitaxel and cisplatin and/or docetaxel and cisplatin. LP-100 has previously been in a genomic signature guided phase 2 clinical
trial in Denmark for patients with metastatic castration resistant prostate cancer (mCRPC). 9 patients (out of a targeted enrollment
of 27) were treated in the trial. The median overall survival (OS) for the initial group of 9 patients was approximately 12.5 months,
which is an improvement over other similar fourth-line treatment regimens for mCRPC. Based on our evaluation of the synergies of LP-100
with PARP inhibitors, the decision was made in the first quarter of 2023 to close the phase 2 clinical trial in Denmark, to allow the
focus of LP-100-directed resources on positioning the molecule for development in earlier lines of therapy with potentially larger market
opportunities. LP-100 was previously out-licensed by us to Allarity Therapeutics A/S. In July 2021, we entered into an Asset Purchase
Agreement to reacquire global development and commercialization rights for LP-100 from Allarity.
Our
development strategy is to pursue an increasing number of oncology focused, molecularly targeted therapies where artificial intelligence
and genomic data can help us provide biological insights, reduce the risk associated with development efforts and help clarify potential
patient response. We plan on strategically evaluating these on a program-by-program basis as they advance into clinical development,
either to be done entirely by us or with out-licensing partners to maximize the commercial opportunity and reduce the time it takes to
bring the right drug to the right patient.
As
part of our overall growth strategy, we plan to grow our pipeline by identifying new drug candidates and pursuing potential indications
for LP-300, LP-184, LP-284, our ADC program and other drug candidates while leveraging our RADR® platform. We are also
pursuing the identification and design of potential combination therapies in cancer for our compounds by leveraging our RADR®
platform to analyze synergistic genomic networks and biological pathways with other currently approved drugs.
In addition, in 2026 we plan to introduce our proprietary artificial intelligence (AI) platforms and related technologies
as a potential source of revenue. This initiative is intended to leverage our existing AI infrastructure, technologies and drug development
expertise to create new opportunities in precision oncology and translational research support. We expect to evaluate multiple partnership
and commercialization models as our platforms advance and approach market readiness through both subscriptions, access and services for
biopharma companies, researchers, drug developers, and other users.
We
have an extensive multi-national portfolio of intellectual property directed to our drug candidates, and to protect the targeted use
and development of our portfolio of compounds in specific patient populations and in specific therapeutic indications. In addition, as
our RADR® platform and other machine learning driven methodologies progress and mature, we will continue to evaluate additional
ways to further protect these assets.
As
of March 1, 2026, we own or control over 200 active patents and patent applications across 20 patent families whose claims are directed
to our drug candidates and what we plan to do with our drug candidates. We have in-licensed or acquired patents and patent applications
from AF Chemicals and BioNumerik Pharmaceuticals that are directed to the compounds LP-184, LP-284, LP-100 and LP-300, and methods of
using the compounds. Additionally, we have also filed patent applications to further enhance and extend the use of these compounds. Our
patent families are directed to our drug candidates, their usage, manufacturing and other matters. These matters are essential to precision
oncology and relate to: (a) data-driven, biologically relevant biomarker signatures, (b) patient selection and stratification approaches
that rely on prediction of response derived from these signatures and, (c) the ability to develop novel, combination therapy approaches
with existing therapeutics.
Our
Drug Candidate Pipeline
One
of the ways we built our drug candidate pipeline is by in-licensing clinical stage drug candidates that may have been discontinued for
development. We use our RADR® platform to assist in analyzing prior clinical research conducted by others to identify
small-molecule oncology drug candidates that have (i) a well-tolerated profile evidenced by completion of phase 1 clinical trials, and
(ii) demonstrated at least limited antitumor or anticancer activity in clinical trials. We intend to advance the drug candidates in our
pipeline as potential precision medicine treatments for cancer. Our targeted development workflow includes preclinical studies where
drug activity and associated gene signatures are identified, in part through strategic collaborations with some of the top academic institutions
and clinical translational centers in the world. Using this collaborative approach, together with innovative observations from our RADR®
platform, we intend to develop and add drug candidates for our pipeline with the objective of treating the right patient populations
with the right oncology therapies.
Our
current pipeline of development programs includes our three lead small molecule drug candidates: LP-300, LP-184, and LP-284, and our
Antibody Drug Conjugate (ADC) program.
We
currently have INDs in the U.S. for LP-300, LP-184 and LP-284.
Our
Precision Cancer Therapy Development Using Our Innovative RADR®Platform
We
believe RADR® is one of the world’s largest A.I and machine learning (M.L.) oncology drug discovery and development
platforms, consisting of over 200 billion oncology-focused data points. These data points consist of large-scale multi-omic data, derived
from over 130,000 patient records, over 150 drug-tumor interactions, thousands of drug classes, and covering over 135 cancer subtypes.
RADR® leverages this data and over 200 advanced ML algorithms to power its drug discovery and development modules. RADR®’s
data, capabilities, and insights have powered the development of new Lantern drug candidates, advancement of new indications for existing
drugs, and identification of potential new drug combinations.
Historically,
cancer treatment protocols include surgery, chemotherapy and radiation therapy. Treatments have been selected based on histologic type
and disease spread, irrespective of genetic differences among patients. With the advent of precision therapies, cancer treatments increasingly
target specific genes or mechanisms of action for a more personalized approach to patient care. This trend represents a substantial advance
in cancer treatment because tumor growth is highly dependent on genetic changes and the genetic profile of the individual and the progression
of the disease is highly variable amongst patients.
Our
RADR® platform is core to our drug development approach for identifying the desired candidates to in-license and develop.
Oncology drug development is exceedingly challenging, with an overall estimated Phase 1-to-approval probability of success of just 3.3
percent (according to reports from DIA Global Forum: What Are the Chances of Getting a Cancer Drug Approved?, May 2019)
and an estimated mean cost to deliver a new oncology medicine of $4.4 billion (Study published in Targeted Oncology, 2023; Analysis
of the Cost of Developing Oncology Drugs Approved by the FDA Between 1997 and 2020). There is a critical need to rescue clinical
research on drugs that have failed clinical trials in order to provide additional possible therapies for patients while reducing the
overall cost of therapeutic development. Many drug failures within oncology may be attributed to the heterogeneity of the tested patient
population, even though there may be a strongly positive therapeutic impact on certain patient subgroups within that population.
As
data-centric and machine learning approaches are beginning to change the pace and scale of drug discovery and development, research and
development (“R&D”) we believe efforts in large biopharma companies are beginning to shift away from traditional approaches
towards new data and A.I.-centric approaches. According to Deloitte Consulting, in Ten Years On | Measuring the return from pharmaceutical
innovation 2019, “decades of advances in science and technology have driven improvements in health care outcomes and influenced
stakeholder expectations of the role of the biopharmaceutical industry (biopharma)”. The Deloitte Consulting report further describes
that R&D costs will, “shift from traditional discovery and trial execution to a process driven by large datasets, advanced
computing power and cloud storage”. Continuing the trend of scientific advancement impacting biopharma, as noted in Deloitte’s
2019 report, the findings in Deloitte’s 2023 report show R&D returns rebounding, with regulatory challenges and the need for
AI integration remaining as key future hurdles for the industry.
Analysts
estimate that this shift from traditional screening, and trial-based studies to leveraging in silico, data and A.I. methodologies has
driven a significant increase in the spending on A.I. by the biopharma and drug discovery community. According to GlobalData, the drive
to reduce drug development time and costs through AI-enhanced computer-aided drug design, coupled with a surge of AI-focused startups,
is projected to result in biopharma AI spending reaching $3 billion by 2025. As a result of these trends and changes in the R&D model
in biopharma, we believe that we, and companies that are using data-centric and A.I. centric approaches to drug discovery and development,
are in an ideal position to benefit from this industry shift that has the potential to help deliver drugs to the right patients faster,
with a higher degree of personalization and a potentially lower amount of average costs in the development cycle.
Our
drug rescue approach leverages substantial prior research and development investments in candidates that were withdrawn from development
prior to submission for FDA approval. The large volume of failed compounds, recent developments that permit increased access to validated
genomic and biomarker data, and the rapid evolution of A.I. technology creates an opportunity to efficiently capitalize on these investments.
Our
RADR® platform is rapidly emerging as a robust and scalable platform for targeted cancer therapy development. Through
the use of A.I., machine learning, and multi-agentic research systems, RADR® is designed to quickly identify and guide
the development of compounds that we can develop as potential oncology agents through either a process of drug rescue, drug repositioning
or de-novo development. RADR® is being developed through an accumulation and curation of genomic data, biomarker data,
chemical structural data, and detailed ontologically indexed documents that are directly relevant to the measurement and classification
of drug-tumor interaction, clinical datapoints related to patient response and patient stratification, and de-novo drug design.
Predicting
optimal drug responses in cancer patients requires the identification and validation of predictive biomarkers. Our RADR® platform
seeks to identify biomarkers to assist in selecting patients who have the highest likelihood to respond to our drug candidates. For example,
the targeted indications for our drug candidate LP-184 were selected in part because they are known to highly express the protein coding
gene PTGR1. Our clinical development plans for LP-184 are intended to provide additional information regarding biomarkers related to
LP-184’s molecular and cellular targets. This method of using and validating targeted biomarkers during development and then using
these biomarkers during future clinical trials can lead to shortening of the development timeline and compression of costs associated
with oncology drug development.
Similarly,
we believe LP-300 targets molecular pathways that are more common in never smokers than in other groups and also targets kinases involved
in key signaling pathways involving enzymes critical for DNA synthesis and repair, such as Excision Repair Cross-Complementation Group
1 (ERCC1), Ribonucleotide Reductase 1 (RNR1), Ribonucleotide Reductase 2 (RNR2), as well as enzymes and proteins important in regulating
cell redox status, such as Thioredoxin (TRX), Peroxiredoxin (PRX), Glutaredoxin (GRX), and Protein Disulfide Isomerase (PDI).
Our
RADR® Platform
The
human genome consists of 19,000 to 20,000 protein coding genes. One input record derived from available data bases and analyzed by our
RADR® platform consists of datapoints (expression values) from approximately 20,000 genes, another input record type is
drug sensitivity data (IC20, IC50), and other sets include key clinical parameters from HIPAA compliant patient data and clinical histories.
Our RADR® platform uses a data-driven gene feature selection methodology that is a combination of biology, informatics,
and statistics – computational biology. The architecture, tools and software of our platform are depicted in the figures below.
We
developed our platform using primarily open-source supervised algorithms such as Neural Networks, Support Vector Machine, Random Forest,
K-Nearest Neighbors, Logistic Regression and Penalized Multivariate Regression. Each algorithm is trained with labeled input data to
predict characteristics such as drug sensitivity (regressor models), stratify patient response as responder or non-responder (classifier
models), or drug behaviors such as ability to cross the blood-brain barrier (ensemble classification). Model tuning and optimization
is then performed using a hyperparameter search algorithm in order to produce the predicted lowest cross validation error. The models
are then evaluated using traditional performance metrics such as accuracy, area under the curve, sensitivity, specificity, precision,
root mean square error and mean absolute error calculations.
A
feature reduction algorithm is used to reduce the number of genes under analysis to a biomarker gene panel of less than approximately
50 genes. This set of genes is intended to carry the highest coefficient to predict drug sensitivity and the highest variable importance
in classifying a responder from a non-responder. Genes that do not help in predicting the output variable are eliminated to allow for
better prediction generalization and understand mechanisms based on key genes only.
Our
RADR®Platform Workflow
Our
RADR® platform’s proprietary workflow involves preliminary statistical analysis on approximately 18,000 features
typically from whole transcriptomic datasets and then reducing the set to approximately 2,000 features. This is followed by gene filtering
via biological and statistical methodologies yielding approximately 200 significant genes. The platform currently contains multiple feature
selection methods and multiple machine learning methods to analyze the drug and omics data, in order to fine tune the model and get better
and improved prediction accuracy. Feature selection ensures that genes that do not contribute to response prediction are excluded from
the output dataset. The prediction component subsequently applies an A.I.-driven reduction algorithm to the previously filtered genes
generating a targeted set of typically less than 50 candidate biomarkers predictive of response to a particular molecule. The figure
below illustrates RADR®’s workflow.
A
distinct and unique benefit of the RADR® platform is its ability to integrate biological knowledge and data-driven feature
selection to generate hypothesis-free biomarker signatures. This can then aid in identifying novel targets for predictive screening and
drug development.
Our
RADR® platform is enabled through access to, and analysis of, a number of key datasets: (i) publicly available databases,
(ii) data from commercial clinical studies and trials and (iii) our proprietary data generated from ex vivo 3D tumor models specific
to drug-tumor interactions. We incorporate automated supervised machine learning strategies along with big data analytics, statistics
and systems biology to facilitate identification of new correlations of genetic biomarkers with drug activity.
The
value of the platform architecture is derived from its validation through the analysis of over 200 billion oncology-specific clinical
and preclinical data points, more than 154 drug-cancer interactions, thousands of drug classes, data covering more than 135 cancer subtypes,
and over 130,000 patient records from 16 databases, one of which is our internal database. RADR® leverages this data and
over 200+ advanced ML algorithms to power its drug discovery and development modules. Our target objectives for additional data growth
efforts of the RADR® platform include a focus on drug sensitivity data, combination treatment outcome data, biomarker
data in rare cancers, and on emerging synthetic lethal targets that are aimed at accelerating the development of new therapies. Additionally,
the RADR® platform’s generative A.I. capabilities, focusing on molecular optimization and automated feature extraction
to improve understanding and prediction of molecular dynamics, safety, and drug-drug interactions are planned to increase in functionality
and scope for both small molecule development and for ADC development, analytics and characterization.
We
use cancer cell line gene expression profiles and drug sensitivity data (IC50) as one of the RADR® platform’s input
types. In a population of 10 case studies our platform was able to distinguish responders from non-responders with an average historical
accuracy of over 80%. We have also used our platform to generate genetic signatures that we believe to have applicability for the majority
of FDA approved drug-tumor indications. External validation, through retrospective data analysis, of patient datasets from 10 independent
clinical studies achieved an average response prediction accuracy greater than 80%, and internal analysis of 120 drug-tumor interactions
in cell lines achieved an accuracy of greater than 85%. The figure below illustrates examples of RADR®’s algorithms
and how they can be used.
We
have developed our platform in a cloud environment that efficiently uses parallel processing to analyze patient stratification and biomarker
selection. Best software engineering practices are followed while designing and developing our platform’s architecture. In order
to track modifications in the software, a version control system is in place. We use a software release process, including a rigorous
regression testing process, to ensure functions and programs are working as designed.
Our
platform uses a simple user input and GUI based AI architecture that can be used in many pharmaceutical research areas such as biomarker
identification, patient stratification, drug rescue and reposition by bioinformaticians, clinicians and trained wet-lab scientists.
In
late 2021, the Code Ocean Platform, a secure cloud-based computing environment manager, was integrated into RADR®. The
Code Ocean environment has upgraded RADR®’s data organization, synchronization, scalability and accessibility. These
architecture changes have enhanced the reproducibility of RADR® aided insights and analysis and created an environment
that improves the ability to collaborate and share insights within Lantern and with Lantern’s collaborators.
In
2025 and early 2026, RADR introduced two AI services intended to serve the needs of clinical researchers and biomedical scientists by
predicting which drugs can cross the blood brain barrier, and a rapid research “co-scientist” collaborator for research in
rare cancers and drug development. Both of these services provide an initial free introductory experience, with an opportunity for further
use through collaborations.
In 2026 we also plan to introduce our proprietary artificial intelligence (AI) platforms and related technologies
as a potential source of revenue. This initiative is intended to leverage our existing AI infrastructure, technologies and drug development
expertise to create new opportunities in precision oncology and translational research support. We expect to evaluate multiple partnership
and commercialization models as our platforms advance and approach market readiness through both subscriptions, access and services for
biopharma companies, researchers, drug developers, and other users.
PredictBBB
web application
PredictBBB
allows the prediction of which small molecules will cross the blood-brain barrier. It is one of many drug development modules that
we have created through the use of RADR®. With
94% accuracy in predicting which small molecules will cross the blood-brain barrier, users only need to provide a SMILES string
which represents the chemical’s structure in order to obtain a prediction. A drug name search bar provides users an easy
method to look up SMILES strings from PubChem and click to use it as the input for predictions, or users can provide their own
SMILES string for proprietary compounds. The model is based on over 4,000 molecular characteristics derived from the chemical
structure which the web application automatically generates and validates for the user input compounds, then returns a prediction
and report in less than approximately 1 minute.
withZeta.AI Rare Cancer Research Platform
withZeta
is a novel generative AI platform built to empower researchers and clinicians to dramatically improve the quality and reduce the time
of rare cancer research, therapeutic development, drug repurposing, biomarker targeting, and clinical trial design.
Built
securely in the AWS Cloud with a serverless, Lambda-based architecture and state-of-the-art large language model (LLM) inference, withZeta integrates an indexed
ontology of 438 rare cancer types, over 559,000 clinical trial records, over 204,000 published papers pertaining to rare cancers, and
532 FDA-approved oncology drug records. In conjunction with these curated databases, Zeta also utilizes a suite of tools including open-weight
specialized language models for de-novo chemical design, integration of predictBBB, molecular feature descriptions, and a collection
of targeted external resources which seamlessly integrate into a unified chat interface through task-aligned system prompts.
Actuate
Therapeutics Collaboration Utilizing RADR Platform
In
May 2021, we entered into a Collaboration Agreement with Actuate Therapeutics, Inc. (“Actuate”), a clinical stage private
biopharmaceutical company focused on the development of compounds for use in the treatment of cancer, and inflammatory diseases leading
to fibrosis. Pursuant to the agreement, we collaborated on utilization of our RADR® platform to develop novel biomarker
derived signatures for use with one of Actuate’s product candidates. As part of the collaboration, we received shares of Actuate
stock subject to meeting certain conditions of the collaboration, as well as the potential to receive additional Actuate stock if results
from the collaboration are utilized in future development efforts.
TTC
Oncology Collaboration to Expand the Clinical Development of Drug Candidate TTC-352
In
February 2023, we entered into a Collaboration Agreement with TTC Oncology (“TTC”). The collaboration focused on using RADR®
to accelerate and sharpen the drug development of TTC’s Phase 2 ready drug candidate TTC-352. TTC-352, is a novel, first-
and best-in-class selective human estrogen receptor (ER) partial agonist (ShERPA) for the treatment of patients with metastatic ER+ breast
cancer. TTC-352 was evaluated in a Phase 1 accelerated dose escalation study for hormone receptor positive metastatic breast cancer,
and it showed early anti-tumor activity signals in heavily pretreated hormone refractory patients. The aims of the collaboration were
to 1) identify biomarker or gene signatures to power potential patient selection for a planned TTC-352 Phase 2 clinical trial, 2) further
characterize TTC-352’s mechanism of action, and 3) discover additional treatment indications for TTC-352.
Oregon
Therapeutics Collaboration to Optimize Precision Development of Drug Candidate XCE853
In
mid-2024, we entered into a strategic A.I.-driven collaboration with French biotechnology company, Oregon Therapeutics, to optimize the
development of its first-in-class protein disulfide isomerase (PDI) inhibitor drug candidate XCE853 in novel and targeted cancer indications.
As part of the collaboration, we leveraged our proprietary RADR® A.I. platform to uncover biomarkers and anticancer-associated
signatures of XCE853 across solid tumors aimed at assisting in precision development. Oregon Therapeutics has focused on developing XCE853
in various cancer indications, including drug-resistant ovarian and pancreatic cancer, certain hematological cancers and several pediatric
cancers including CNS cancers. The objectives of the collaboration included a focus on 1) uncovering biomarkers and efficacy-associated
gene signatures to guide in the eventual stratification and selection of patients for future clinical trials, 2) efforts to identify
tumor-based response and resistance mechanisms to XCE853 and strategies to overcome treatment resistance, and 3) identification of opportunities
to expand the use of XCE853 in additional therapeutic cancer indications for XCE853.
Our
Strategy
Our
mission is to bring the right cancer drugs to the right patients by transforming the drug development process through the use of artificial
intelligence and data-driven development approaches. Our proprietary A.I.-enabled, and precision oncology approach, which focuses on
developing our own pipeline of compounds by rescuing drug candidates that have previously failed and developing new compounds that are
targeted to specific biological activity and genomic pathways, has the potential, we believe, to bring drugs to market faster, with lower
costs, and with reduced risk, thereby enabling a change in the cost and availability of precision cancer therapy. The strength of this
approach is demonstrated by our track record of advancing newly developed drug programs from initial AI insights to first-in-human clinical
trials in 2–3 years and at approximately $1.0 – $2.5 million per program. We work with leading research laboratories, translational
medicine and cancer centers to develop our studies and clinical trials for our portfolio, and actively update and improve our RADR®
platform to incorporate additional biomarker data, patient outcome data, cancer drug efficacy studies and computational models that relate
to oncology drug development and prediction of patient response. Our RADR® platform has grown to over 200 billion oncology-focused
data points and a library of 200+ advanced machine learning algorithms, and we are actively expanding its capabilities through new modules
focused on ADC development, combination regimen prediction, and blood-brain barrier permeability assessment.
As
part of our strategy, we plan to:
LP-300
General
Overview
We
are currently advancing LP-300 in a Phase 2 clinical trial (the “HARMONICTM Study”) of LP-300 in combination with carboplatin
and pemetrexed in never smoker patients with relapsed advanced primary adenocarcinoma of the lung after treatment with tyrosine kinase
inhibitors (TKIs).
LP-300
is a cysteine-modifying molecular entity that works to modulate multiple cellular pathways simultaneously and is a potential combination
agent for targeted indications in NSCLC. LP-300 is a small molecule (molecular weight 326.4 Da) that was in-licensed from BioNumerik
Pharmaceuticals, Inc. in May 2016, and subsequently acquired by us in 2018. We are focused on repositioning LP-300 as a potential combination
therapy for never smoker NSCLC patients with histologically defined adenocarcinoma. Prior clinical trials conducted by BioNumerik for
LP-300 did not meet their primary clinical endpoints, and at least one or more future clinical trials that meet their pre-specified primary
endpoints with statistical significance will be required before we can obtain a regulatory marketing approval, if any, to commercialize
LP-300. Safety and efficacy determinations are solely within the authority of the FDA in the U.S. or other regulatory agencies in other
jurisdictions. Currently there is no approved therapy specifically for the growing indication of never-smokers with NSCLC, and female
never smokers appear to be uniquely responsive to LP-300. With both chemosensitizing and chemoprotective activity, LP-300 has potential
as a combination agent or adjuvant in front line, second line or salvage therapy in newly diagnosed, relapsed, metastatic or advanced
NSCLC for overall survival enhancement and toxicity alleviation from primary chemotherapy or standard of care. We are currently in the
early stages of defining a specific biomarker signature that correlates with heightened sensitivity to LP-300. We believe that this signature
may help accelerate the clinical development of LP-300 and has the potential to guide patient selection for targeted clinical trials.
Prior
clinical trials conducted by BioNumerik for LP-300 did not meet their primary clinical endpoints and at least one or more future clinical
trials that meet their pre-specified primary endpoints with statistical significance will be required before we can obtain a regulatory
marketing approval, if any, to commercialize LP-300. Prior clinical trial observations are not necessarily predictive of the outcome
of any future clinical trials we may conduct.
LP-300
has been administered in multiple clinical trials to more than 1,000 subjects and has been generally well-tolerated. Retrospective analyses
of the results of a multi-country phase 3 lung cancer trial (study ID DMS32212R) in subgroups of adenocarcinoma patients receiving LP-300,
paclitaxel and cisplatin demonstrated substantial improvement in overall survival, particularly among female never smokers, where a 13.6
month improvement in overall survival (p-value 0.0167, hazard ratio 0.367) in favor of LP-300 was observed, as compared to placebo in
the subgroup of paclitaxel/cisplatin-treated patients. Similar retrospective findings of increased overall survival in the subgroup of
LP-300/paclitaxel/cisplatin treated female Asian patients with adenocarcinoma of the lung were observed in a randomized, double-blind,
placebo-controlled trial in Japan. Prior historical clinical trial observations are not necessarily predictive of the outcome of future
trials. No assurances can be given that we will be successful in obtaining marketing approval for LP-300. The chemical structure of LP-300
is depicted below.
LP-300
Chemical Structure
The
Ongoing HARMONICTM Study
We
are conducting a Phase 2 clinical trial (the “HARMONICTM Study”) of LP-300 in combination with carboplatin and pemetrexed
in never smoker patients with relapsed advanced primary adenocarcinoma of the lung after treatment with tyrosine kinase inhibitors. Our
purpose in conducting the study is to determine the potential clinical advantages and benefits for this drug combination in the study-defined
patient population. As of March 17, 2026, we have 4 clinical trial sites in the US, 5 clinical trial sites in Japan, and 5 clinical trial
sites in Taiwan. Enrollment of patients on the HarmonicTM Study in the U.S. has been challenging, and we have implemented a strategy
of increasing enrollment by expanding the study to East Asian countries where approximately 30-35+% of all lung cancer cases occur in
never-smokers with NSCLC.
The
HarmonicTM Study is designed as a multicenter, open label, Phase 2 trial with planned total enrollment of approximately 90 patients.
Patients who are never smokers with lung adenocarcinoma and have relapsed after prior treatment with tyrosine kinase inhibitors will
be eligible for enrollment. Patients who are former smokers but carry actionable genomic alteration(s) may also be eligible. Following