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, 2021
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. ☐
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: $121,719,704.
As of March 1, 2022, the registrant had 10,780,490 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 2022 Annual Meeting of Stockholders to be filed pursuant to Regulation 14A within 120 days
of the registrant’s year ended December 31, 2021 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 56
Item 1B. Unresolved Staff Comments. 102
Item 2. Properties. 102
Item 3. Legal Proceedings. 102
Item 4. Mine Safety Disclosures. 102
PART II
Item 6. Reserved. 105
Item 7A. Quantitative and Qualitative Disclosures About Market Risk. 113
Item 8. Financial Statements and Supplementary Data. F-1
Item 9A. Controls and Procedures. 114
Item 9B. Other Information. 114
PART III
Item 10. Directors, Executive Officers and Corporate Governance. 115
Item 11. Executive Compensation. 115
Item 14. Principal Accountant Fees and Services. 115
PART IV
Item 15. Exhibit and Financial Statement Schedules. 116
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,” "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;
● the potential impact that COVID-19 may have on our business plans;
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 18 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 millions 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.
1
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, one
in preclinical studies and one candidate and our ADC program in research optimization. One of the two drug candidates in clinical development,
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. 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 preparing LP-300 for the launch of a targeted
phase II trial, in never smoking patients with NSCLC in combination with chemotherapy, under an existing investigational new drug application.
LP-100 is in a Phase II clinical trial in metastatic, castration-resistant, prostate cancer that was previously managed by Allarity Therapeutics.
As a result of the Asset Purchase Agreement we entered into with Allarity in July 2021, we obtained full authority to manage and guide
future clinical development and commercialization of LP-100.
Additionally,
we have one new drug candidate, LP-184, in 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 the drug, if approved. Subject to regulatory clearance to move forward under a future IND application, we are planning
a Phase I clinical trial for LP-184 to begin later in 2022. Our drug candidate, LP-284, the stereoisomer (enantiomer) of LP-184, has shown
promising in-vitro anticancer activity in a range of hematological cancers, which are distinct from the indications targeted
by LP-184. Our ADC program commenced in early 2021 is aimed at identifying targeted or therapeutic antibodies to conjugate with selected
compounds.
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.
2
As part of our overall growth
strategy, we plan to grow our pipeline by identifying new drug candidates and pursuing potential indications for LP-184, LP-300, 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 2022, we own or control over 80 active patents and patent applications across 14 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 from AF Chemicals, and BioNumerik
that are directed to the compounds, LP-100, LP-184 and LP-300. 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) uniquely powerful, 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.
We use our RADR®
platform to identify potential biomarkers for patient response to a drug candidate and we further intend to validate the selected
drug candidate and potential associated biomarkers by conducting small, focused early phase clinical trials. We intend to create various
exit opportunities for each drug candidate that progresses successfully. For each drug candidate that progresses, along with its newly
identified biomarker diagnostic potential for drug response, we intend to partner, out-license, or internally develop the drug.
3
Our current pipeline of development
programs involves four small molecule drug candidates: LP-100, LP-300, 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
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.
4
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 a decade 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 by 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 AI 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 AI 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 on
a routine basis 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).
5
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 and modules of our platform are depicted in the image below.
6
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.
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.
7
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 18 billion oncology-specific clinical and preclinical data points,
more than 154 drug-cancer interactions, and over 100,000 patient records from 16 databases, one of which is our internal database. Our
long-term objective is to collect and analyze over 25 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%.
8
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. Each component of the platform’s
architecture is unit tested and then integration tested to ensure functions and programs are working as designed. In order to track modifications
in the software, a version control system is in place. Detailed documentation has been created to record the design and architecture of
our platform.
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.
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 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.
9
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.
10
LP-300
General Overview
We are currently advancing
LP-300 towards launch of 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 non-smoking (or
never-smoker) NSCLC patients with histologically defined adenocarcinoma. Since obtaining LP-300 rights from BioNumerik, we have not yet
conducted further clinical testing of LP-300. 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 patients 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
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Upcoming LP-300
Phase II Clinical Trial
We
are preparing to launch 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.
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 will consist 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:
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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.
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Rationale Behind LP-300 Rescue and Repositioning
Efforts
Based on the results from
the prior Phase III NSCL adenocarcinoma trial, we are preparing to launch a new 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 13% of all new cancer diagnoses, but 25% of all cancer deaths.
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 2022 are:
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The most common type of lung
cancer is called non-small cell lung cancer (“NSCLC”), which represents about 84% 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 the SEER Cancer
Statistics Review (November 2018) published by the National Cancer Institute 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 8 to 10 months; approximately one-third of patients will survive for 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. Mutant BRAF and ERBB2 are also investigational targets. 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. There are
no current KRAS-mutated NSCLC-targeted therapies but there are targeted therapies for the indication by targeting downstream pathways
- for example mTOR inhibition. Tumor suppressor gene abnormalities, such as those in TP53, STK11, 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.
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In the US in 2021, there were an estimated 15,750
diagnosed cases of NSCLC in female non-smokers, accounting for approximately 6.7% of all lung cancer cases. Globally in 2021, there were
an estimated 147,027 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 2018 estimates published by Global Cancer Observatory
and 2021 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:
Lung cancer Global US
Total Potential Patient Segment in New Lung Cancer 5% - 8 % 5% - 8 %
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.
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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.