Item 1A. Risk Factors 60
Item 1B. Unresolved Staff Comments. 109
Item 2. Properties. 109
Item 3. Legal Proceedings. 109
Item 4. Mine Safety Disclosures. 109
PART II
Item 6. Reserved. 111
Item 7A. Quantitative and Qualitative Disclosures About Market Risk. 120
Item 8. Financial Statements and Supplementary Data. F-1
Item 9A. Controls and Procedures. 121
Item 9B. Other Information. 122
PART III
Item 10. Directors, Executive Officers and Corporate Governance. 123
Item 11. Executive Compensation. 123
Item 14. Principal Accountant Fees and Services. 123
PART IV
Item 15. Exhibit and Financial Statement Schedules. 124
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 studies and clinical trials, 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 a clinical stage biotechnology company, focused on leveraging artificial intelligence (“A.I.”), machine learning and
genomic data to streamline the drug development process and to identify the patients that will benefit from our targeted oncology therapies.
Our portfolio of therapies 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 25 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.
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 (i.e., a meaningful treatment benefit relevant for the disease
or condition under study as measured against the comparator treatment used in the relevant clinical testing) 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 (absorption, distribution, metabolism,
and excretion) studies that have been completed. Additionally, these drug candidates may also have a body of existing data supporting
the potential mechanism(s) by which they achieve their intended biologic effect, but often require more targeted trials in a stratified
group of patients to demonstrate statistically meaningful results. 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 intend 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. We believe the combination of our therapeutic
area expertise, our A.I. expertise, and our ability to identify and develop promising drug candidates through our collaborative relationships
with research institutions in selected areas of oncology gives us a significant competitive advantage. Our RADR® platform
was developed and refined over the last five years and integrates billions of data points immediately relevant for oncology drug development
and patient response prediction using artificial intelligence and proprietary machine learning algorithms. By identifying clinical candidates,
together with relevant genomic and phenotypic data, we believe our approach will help us design more efficient preclinical studies, and
more targeted clinical trials, thereby accelerating our drug candidates’ time to approval and eventually to market. Although we
have not yet applied for or received regulatory or marketing approval for any of our drug candidates, we believe our RADR®
platform has the ability to reduce the cost and time to bring drug candidates to specifically targeted patient groups. We believe we
have developed a sustainable and scalable biopharma business model by combining a unique, oncology-focused big-data platform that leverages
artificial intelligence along with active clinical and preclinical programs that are being advanced in targeted cancer therapeutic areas
to address today’s treatment needs.
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 four compounds and an Antibody Drug Conjugate (ADC) program: two drug candidates in clinical phases, two
in the pre-IND preclinical stage and our ADC program in research optimization. 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
currently have two drug candidates in clinical development, LP-100 and LP-300, where we are leveraging 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 III
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, and we are currently conducting
a targeted phase II trial (the HarmonicTM trial) for LP-300 in never smoking patients with NSCLC in combination with chemotherapy,
under an existing investigational new drug application. 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.
Additionally,
we have two new drug candidates, LP-184 and LP-284, in pre-IND preclinical development for multiple potentially distinct indications
where we are leveraging machine learning and genomic data to streamline the drug development process and to identify the patients
and cancer subtypes that will best benefit from these drugs, if approved. Subject to regulatory clearance to move forward under
future IND applications, we are planning a Phase I clinical trial for LP-184 to begin in mid 2023 and a Phase I clinical trial for
LP-284 to begin in mid 2023. Our ADC program commenced in early 2021 is aimed at identifying targeted or therapeutic antibodies to
conjugate with selected compounds. 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 will refer to the molecule LP-184, as it is developed in CNS indications, as
“STAR-001”.
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, and LP-100 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.
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, 2023, we own or control over 80 active patents and patent applications across over 16 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 that are directed to the compounds, LP-100, LP-184, LP-284 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 in-licensed compounds. Our 14 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 are building 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 I 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 to our pipeline with the objective of treating the right patient populations
with the right oncology therapies.
Our
current pipeline of development programs involves four small molecule drug candidates: LP-300, LP-100, LP-184, and LP-284, and an Antibody
Drug Conjugate (ADC) program.
We
currently have an existing IND in the U.S. for LP-300 that was transferred to us as part of our in-licensing and agreement with BioNumerik
to acquire the rights to the compound. There is currently no active IND in the U.S. for LP-100, LP-184 and LP-284.
Our
Precision Cancer Therapy Development Using Our Innovative RADR®Platform
RADR®
is one of the world’s largest A.I. and machine learning (M.L.) oncology drug discovery and development platforms, consisting
of over 25+ billion oncology-focused data points. These data points consist of large-scale multi-omic data, derived from 130,000+ patient
records, 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.
According to a recent article in JAMA (Estimated Research and Development Investment Needed to Bring a New Medicine to Market,
2009-18, JAMA, March 3, 2020) oncology drug development is costly, risky, and highly competitive with an average success rate
of 4% to 8% and average developmental costs of over $1 billion per successful drug. 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 begin to change the pace and scale of drug discovery and development, research and development
(“R&D”) we believe efforts in large biopharma companies will begin 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). However, the past decade has seen increasing pressures
undermine the productivity of biopharma R&D, leading to multiple years of decline in the return on investment. At the same time,
innovative new treatments are changing the face of disease management. New treatment modalities and an increasing understanding of precision
medicine have led to the need for new R&D models...” 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”.
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 to approximately $4 billion in 2021,
increasing by about 40% annually from $730 million in 2019 according to PMLive and Global Market Insights. 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. and machine learning, 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 and biomarker data that is directly relevant to the measurement
and classification drug-tumor interaction, and clinical datapoints related to patient response and patient stratification.
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 chosen in part because they are known to highly express the protein coding
gene PTGR1. Our planned clinical trial for LP-184 is 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 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 third party 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 input data
to predict drug sensitivity (regressor models) and stratify patient response as responder or non-responder (classifier models). 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 then 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 sequentially.
Our
RADR® Platform Workflow
Our
RADR® platform’s proprietary workflow involves preliminary statistical analysis on approximately 18,000 features
typically from whole transcriptomic datasets 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 6 feature selection
methods and 13 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 25 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 long-term objective is to collect and analyze
over 100 billion oncology-specific clinical and preclinical data points to further enhance the prediction power of our RADR®
platform. We use cancer cell line gene expression profiles and drug sensitivity data (IC50) as one of its 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. The figure below illustrates ways that RADR®’s modules can be used to facilitate drug discovery and
development within Lantern and with our collaborators.
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, as amended, we are collaborating with Actuate 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
25,000 restricted 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 is 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 recently evaluated in a Phase 1 accelerated dose escalation study for hormone receptor positive
metastatic breast cancer, and it showed early efficacy signals in heavily pretreated hormone refractory patients. The initial aims of
the collaboration are to 1) identify biomarker or gene signatures to power potential patient selection for an upcoming TTC-352 Phase
2 clinical trial, 2) further characterize TTC-352’s mechanism of action, and 3) discover additional treatment indications for TTC-352.
Under the terms of the collaboration, Lantern is receiving an exclusive right to license TTC-352, including any collaboration intellectual
property (“IP”), during an exclusive option period. Additionally, Lantern and TTC will each participate in upfront, milestone,
and royalty payments in the event a third-party licenses IP resulting from the collaboration.
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. 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.
As
part of our growth strategy, we plan to:
● Continue to select and launch additional clinical development program.
LP-300
General
Overview
We
are currently advancing LP-300 in a Phase II 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 smokers 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 III 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
LP-300
Phase II Clinical Trial
We
are conducting a Phase II 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 for this drug combination in the study-defined patient
population. As of the date of this report, we have activated 5 clinical trial sites
in the US, across 12 locations, and we anticipate multiple additional sites in the US during the first half of 2023, with first enrolled
patients anticipated in the second quarter of 2023.
The
trial is designed as a multicenter, open label, Phase II trial with planned 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.
Following a six-patient safety lead-in stage, the trial consists of randomization in a 2:1 allocation ratio to one of two arms: Arm A
(consisting of carboplatin, pemetrexed, and LP-300) or Arm B (consisting of carboplatin and pemetrexed).
The
primary objective of this study is to determine progression-free survival and overall survival in the study-defined patient population
when co-administered LP-300 with combination chemotherapy (carboplatin and pemetrexed) versus carboplatin and pemetrexed alone. The secondary
objectives of the study are to evaluate tumor response measured by objective response rate, duration of objective response, and clinical
benefit rate. We will also determine any associations between the efficacy endpoints and patient biomarkers (e.g., circulating tumor
DNA and tumor genome characteristics) as an exploratory objective. Other exploratory objectives for the study may include evaluating
quality of life in all patients and performance of patients based on the type, duration, and number of tyrosine kinase inhibitors received.
Key
Findings from Prior LP-300 Clinical Trials
Summarized
below are some key findings from LP-300’s prior clinical trials:
Background-Scope
of Prior Phase III NSCLC Adenocarcinoma Trial (LP-300)
LP-300
was studied in a randomized, multi-center (trial locations in four US states and five European countries), double-blind and placebo-controlled
Phase III trial from 2010 to 2013 in patients with adenocarcinoma of the lung (the “Phase III NSCLC adenocarcinoma trial”).
The aim of the trial was to determine whether LP-300, combined with a standard combination of chemotherapy drugs, would increase survival
in patients with advanced NSCLC adenocarcinoma. The secondary aim of the trial was to determine if the chemoprotective properties of
LP-300 were effective in preventing or reducing common side-effects of cancer treatment, including kidney damage, anemia, nausea and
vomiting that can occur with these drug combinations. The trial enrolled NSCLC patients with newly diagnosed or recurrent advanced (stage
IIIB/IV) primary adenocarcinoma of the lung. Patients with confirmed histopathological diagnosis of inoperable and measurable advanced
primary adenocarcinoma (including bronchioalveolar cell carcinoma) of the lung, and no prior systemic treatment for NSCLC including chemotherapy,
immunotherapy, hormonal therapy, targeted therapies or investigational drugs, were included in the trial. Overall survival was the primary
outcome measure. Patients in the control arm received standard of care (cisplatin and either paclitaxel or docetaxel) plus placebo, whereas
patients in the treatment arm received standard of care (cisplatin and either paclitaxel or docetaxel) plus LP-300. The primary results
of the trial for patients receiving cisplatin and paclitaxel are outlined in the table below. While the overall results of the Phase
III NSCLC adenocarcinoma trial did not meet the specified endpoint of the trial in increasing overall survival in all patients, when
the data were retrospectively separated by gender and smoking status, the trial data demonstrated that all never smokers, especially
female never smokers, saw increased survival with LP-300 combination treatment with paclitaxel and cisplatin. Furthermore, the LP-300
group in the phase III NSCLC adenocarcinoma trial exhibited well-tolerated advantages relating to the potential to protect against chemotherapy-induced
nephrotoxicity, neuropathy and nausea along with reduced anemia.
The
figure below depicts the survival curves for cisplatin/paclitaxel subgroups for the Phase III NSCLC adenocarcinoma trial that ended in
2013, as summarized. The Kaplan Meier curves maintain consistent separation between treatment arms for the never smokers, females, and
female never smokers.
Rationale
Behind LP-300 Rescue and Repositioning Efforts
Based
on the results from the prior Phase III NSCL adenocarcinoma trial, we have launched the HARMONICTM LP-300 Phase II clinical trial
to target the subpopulation of never smokers with adenocarcinoma that saw strong benefit in the previous Phase III trial. Although the
incidence of never-smokers with NSCLC is rising currently there is no approved therapy specifically for the growing indication of never-smokers
with NSCLC. Preclinical observations support that LP-300 preferentially modulates ALK and EGFR, two commonly mutated genes in non-smokers
with adenocarcinoma. Based on the findings from the previous Phase III NSCL adenocarcinoma trial, it is possible that the benefits of
combining LP-300 with standard of care chemotherapy could be further improved by identifying additional molecular biomarkers in patients
who respond well to LP-300 combination treatment. We continue to seek additional opportunities for LP-300. Some of our considerations
include a never smoker population with a specific genetic signature that correlates to increased LP-300 sensitivity.
Disease
Background and Opportunity
Lung
cancer remains one of the most common and deadly cancers worldwide. Lung cancer accounts for 12% of all new cancer diagnoses, but 21%
of all cancer deaths in the US. Lung cancer kills more people annually than cancers of the breast, prostate, colon, liver, kidney, pancreatic,
and melanoma combined. The American Cancer Society’s estimates for lung cancer in the US for 2023 are:
The
most common type of lung cancer is called non-small cell lung cancer (“NSCLC”), which represents about 80% to 85% of all
lung cancer.
Lung
adenocarcinoma, a histological subtype of NSCLC that originates within the glands that line the lung, is the most common subtype of lung
cancer in the world inflicting approximately 50% to 65% of non-Asians and approximately 70% to 85% of Asians diagnosed with lung cancer.
According to LUNGevity Foundation, the National Institutes of Health and other published literature, 60% to 65% of all new lung cancer
diagnoses are among people who are former smokers or have never smoked, while 10-15% of new lung cancer cases are among never-smokers.
Over
one-half of the patients diagnosed with NSCLC in any given year will present with inoperable advanced (stage IV) disease, for which there
is no cure. Patients with stage IV NSCLC exhibit a median overall survival time of 7 to 12 months; approximately one-third of patients
will survive for a year, and only 10% to 21% of those patients will survive for two years.
Lung
cancer is the most common cause of global cancer-related mortality, leading to over a million deaths each year and adenocarcinoma is
its most common histological subtype. Worldwide, lung cancer occurred in approximately 2.2 million patients in 2020 and caused an estimated
1.8 million deaths. NSCLC is described as any type of epithelial lung cancer other than small cell lung cancer (“SCLC”).
The 5-year survival rate for NSCLC is 25%.
Rapid
advances in understanding the molecular pathogenesis of NSCLC have demonstrated that NSCLC is a heterogeneous group of diseases. Although
the initial treatment of localized disease is the same, the molecular characterization of tumor tissue in patients with NSCLC serves
as a guide to treatment both in those who present with metastatic disease and in those who relapse after primary therapy. Molecularly
targeted therapies have dramatically improved treatment for patients whose tumors harbor somatically activated oncogenes such as mutant
EGFR1 or translocated ALK, RET, or ROS1. Smoking is the major cause of lung adenocarcinoma but, as smoking rates decrease, proportionally
more cases occur in never-smokers (defined as less than 100 cigarettes in a lifetime). KRAS mutations in lung cancer cases are nearly
exclusive to smokers. KRAS, “Kristen rat sarcoma viral oncogene homolog,” is a protein involved in regulating cell division.
KRAS mutation is a gain-of-function mutation (i.e. somatic mutation turns RAS, a benign gene “proto-oncogene” into KRAS,
an oncogenic driver of many tumors). KRAS-mutated non-small cell lung cancer represents 20% to 25% of all NSCLC. FDA granted accelerated
approval to KRAS inhibitor sotorasib and Antibody Drug Conjugate trastuzumab deruxtecan (Enhertu) for KRAS G12C -mutated and HER2
mutated advanced stages non-small cell lung cancer (NSCLC), respectively. In 2022, the combination of CTLA-4 inhibitor tremelimumab and
the anti-PDL1 antibody durvalumab was approved by FDA for treating metastatic NSCLC patients lacking EGFR mutation or ALK translocation.
Tumor suppressor gene abnormalities, such as those in TP53, CDKN2A8, KEAP1, and SMARCA4 are also common but are not currently clinically
actionable.
In
reviewing lung cancer incidence and mortality rates among never-smokers in the Journal of Clinical Oncology, Wakelee, H.A. et al. have
reported that the age-adjusted incidence rates of lung cancer among never-smokers aged 40 to 79 years from large population-based cohorts
ranged from 14.4 to 20.8 per 100,000 person-years in women and 4.8 to 13.7 per 100,000 person-years in men, supporting earlier observations
that women are more likely than men to have never smoking-associated lung cancer. The biology of lung cancer in never-smokers is apparent
in differential responses to epidermal growth factor receptor inhibitors and an increased prevalence of adenocarcinoma histology in never-smokers.
Lung cancer in never-smokers is an important public health issue needing further exploration of its incidence patterns, etiology, and
biology. Due to the fact that there are no known therapy options for this group, we believe that aggressive development of therapy options
is needed and is a high unmet clinical need.
In
the US in 2023, there will be an estimated 12,000 diagnosed cases of NSCLC in female non-smokers, accounting for approximately 5% of
all lung cancer cases. Globally in 2020, there were an estimated 111,583 adenocarcinoma cases of NSCLC in female non-smokers. Due to
the specificity of this indication, it may be possible to classify it as a rare disease. When attempting to explain some gender susceptibility
differences, research has demonstrated that women with NSCLC tend to be:
● Younger;
● Asian;
● 2-3 times more likely to be non-smokers;
● more likely to develop adenocarcinoma and;
● more likely to have metastatic disease.
The
high rate of adenocarcinomas in non-smoking women suggests the possible existence of other etiological factors in addition to smoking.
Some factors that have been considered include gender-specific genetic alterations and predispositions, passive smoke effects, different
nicotine metabolism in women, occupational exposure, diet, and chronic obstructive pulmonary disease. Based upon estimates published
by Global Cancer Statistics 2020 and 2023 estimates published by the American Cancer Society, below is an overview of relevant potential
patient population and market sizes that we believe LP-300 could address, if approved:
Total lung cancer estimated incidence (new cases) 2,210,000 238,340
Total Potential Patient Segment in New Lung Cancer 5 % 5 %
Limitations
on Current Treatment
Treatment
of patients with advanced NSCLC in the first-line setting usually includes chemotherapy (including taxanes, vinorelbine, or gemcitabine)
in combination with a platinum doublet (cisplatin or carboplatin). According to the clinical practice guidelines published by the National
Comprehensive Cancer Network, many of these combinations have reached a plateau in terms of overall response (≥ 25% to 35%), time
to progression (four to six months), median survival time (eight to ten months), one-year survival rate (30% to 40%), and two-year survival
rate (10% to 15%) in patients with good performance status. Treatment remains palliative and is limited due to inherent toxicities that
may affect the quality of life resulting from treatment. Toxicities can be life-threatening or cause treatment delays, thereby limiting
the intensity of treatment delivered and affecting its efficacy. Common and serious chemotherapy-induced toxicities, such as anemia,
emesis, and peripheral neurotoxicity resulting from treatment with platinum and taxanes, and nephrotoxicity due to cisplatin can result
in treatment delays, dose modifications, and in severe cases, discontinuation of treatment.
The
identification of gene mutations in lung cancer has led to the development of molecularly targeted therapy to improve the survival of
subsets of patients with metastatic disease. In particular, genetic abnormalities in EGFR, MAPK, and PI3K signaling
pathways in subsets of NSCLC may define mechanisms of drug sensitivity and primary or acquired resistance to tyrosine kinase inhibitors
(TKIs). To date, approximately 21 TKIs have been approved for use in treating NSCLC with identified tyrosine kinase (TK) mutations; the
TKs targeted by these inhibitors include EGFR, ALK, ROS1, BRAF/MEK, RET, and MET. If patients are found to have specific TK mutations
to which inhibitors are known to respond, treatment with such TKIs is currently standard-of-care for this population of advanced NSCLC.
Most tumors will respond to initial treatment with TKIs, exhibiting tumor shrinking or delayed progression. Unfortunately, most patients
will eventually develop resistance to the inhibitory effects of initial used inhibitors. Therefore, second- or third-line therapy often
involves treatment with alternate inhibitors targeting the same kinase but with differing mutations. Such treatment again is often initially
successful, but further kinase mutations, or mutations arising in different kinases, often leads to relapse and the need to switch to
alternative treatment schemes. This next therapy usually involves chemotherapy (often carboplatin plus pemetrexed), sometimes used in
combination with immunotherapy, or enrollment in clinical trials testing new treatment approaches.
We
believe it is important to pursue the development of novel therapies and combinations thereof that can substantially improve patient
survival and quality of life by potentiating the antitumor activity of chemotherapy treatment while protecting against chemotherapy-induced
toxicity.
Market
Opportunity
Most
never-smoker patients with lung cancer are women, and adenocarcinoma is the most common type. Non-smoker patients with non-small-cell
lung cancer (“NSCLC”) generally have a better response to inhibitors of epidermal-growth-factor receptor (EGFR) tyrosine
kinase, including without limitation gefitinib and erlotinib, than do those with a history of tobacco smoking. Studies have identified
differences in chromosomal aberrations, genetic polymorphisms, gene mutations, and methylation status between lung cancer in non-smokers
and tobacco-associated lung cancer. These clinical and biological differences suggest that the two cancers have overlapping but unique
pathways of carcinogenesis. The EGFR mutation is one of the most important genetic change in lung cancer in people who have never smoked
because it is more common in lung cancer in never-smokers than in tobacco associated lung cancer and is associated with greater therapeutic
benefit from inhibitors of EGFR. Other alterations associated with never-smokers include mutations, fusions or amplifications in ALK,
ROS1, RET and MET genes. Based upon published articles in CA: Cancer Journal for Clinicians and Nature Review Cancer, incidence in never-smokers
is 10% to 15% of all lung cancers and globally, NSCLC in never-smokers comprises 15% to 20% of cases in men and greater than 50% in women.
In Asia, never-smokers with NSCLC are 60% to 80% women and 20% to 40% men.
We
are focused on advancing LP-300 as a potential combination therapy for never smoking NSCLC patients with adenocarcinoma by leveraging
our A.I. platform to help uncover the genomic and biomarker networks that are associated with response in the never-smoker and non-smoker
groups. Additionally, through our early, preclinical work to define a gene signature that correlates with heightened sensitivity to LP-300,
we believe there is potential to further expand the indication to include all NSCLC patients that have this identified genetic profile
in their cancer. 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. If successful, LP-300 could provide improved patient benefit in terms of improved
survival, and secondarily through the concurrent prevention and mitigation of common and serious chemotherapy-induced toxicities.
Prior
Completed Trials of LP-300
Phase
I. LP-300 has been evaluated in five Phase I studies (DMS10001, BioNumerik, 09/1997 through 04/2004; DMS10002, BioNumerik, 12/1997
through 08/2001; DMS12209, ASKA Pharmaceutical, 04/2000 through 12/2001; DMS10011, BioNumerik, 02/2006 through 07/2006; and DMS12307,
Baxter, 07/2002 through 07/2005) to determine the maximum tolerated dose (“MTD”), and to evaluate the safety, tolerability,
pharmacokinetics, and potential efficacy of LP-300 (alone or in combination with cisplatin, cisplatin/paclitaxel, or carboplatin/paclitaxel).
An MTD for LP-300 was not reached in any of the Phase I studies at dose levels of up to 41 g/m2.
Phase
II. In a U.S. multi-center, randomized, open-label trial (n=160 patients) with advanced (Stage IIIB and IV) NSCLC treated with LP-300
or no LP-300 (DMS22210/CALGB 30303, Cancer and Leukemia Group B, 08/2004 through 03/2007), although the overall population did not meet
the pre-specified primary endpoint, an analysis of a subgroup of patients with adenocarcinoma revealed that the difference in the median
overall survival period between the 2 treatment groups was statistically significant (LP-300 = 15.6 months, no LP-300 = 8.9 months; Log-rank
p=0.0326), and the median overall survival for patients who received LP-300 was 6.7 months longer than that of those who did not receive