10-K
1
f10k2020_lanternpharma.htm
ANNUAL REPORT
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, 2020
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: $34,733,370.
As
of March 8, 2021, the registrant had 11,169,665 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 2021 Annual Meeting of Stockholders to be filed
pursuant to Regulation 14A within 120 days of the registrant’s year ended December 31, 2020 are incorporated herein by reference
into Part III of this Annual Report on Form 10-K.
Table
of Contents
Page
Forward Looking Statements ii
Item 1. Business 1
Item 1A. Risk Factors 65
Item 1B. Unresolved Staff Comments. 112
Item 2. Properties. 112
Item 3. Legal Proceedings. 112
Item 4. Mine Safety Disclosures. 112
PART II
Item 6. Selected Financial Data. 114
Item 7A. Quantitative and Qualitative Disclosures About Market Risk. 125
Item 8. Financial Statements and Supplementary Data. F-1
Item 9A. Controls and Procedures. 126
Item 9B. Other Information. 126
PART III
Item 10. Directors, Executive Officers and Corporate Governance. 127
Item 11. Executive Compensation. 127
Item 14. Principal Accountant Fees and Services. 127
PART IV
Item 15. Exhibit and Financial Statement Schedules. 128
i
SPECIAL
NOTE CONCERNING FORWARD-LOOKING STATEMENTS AND RISK FACTORS
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,” “aim,” “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 subsidiary.
iii
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 1.2 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 four 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 in active development: two drug candidates in clinical phases, one in preclinical studies, and one in
research optimization. All of these drug candidates 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 that would 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. Additionally, we have one new drug candidate,
LP-184, in preclinical development for two 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. As part of our antibody drug conjugate (ADC) program commenced in early 2021, we have initiated the optimization
and evaluation of an antibody drug conjugate aimed at leveraging our LP-184 molecule in combination with an antibody for select
solid tumors.
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.
We have out-licensed
our drug-candidate LP-100 to Allarity Therapeutics A/S (“Allarity Therapeutics”), a European biotechnology company.
LP-100 is in a Phase II clinical trial in metastatic, castration-resistant, prostate cancer (mCRPC) that is managed by Allarity
Therapeutics. Our second clinical-stage drug candidate in the rescue process is LP-300. LP-300 is a small molecule with cysteine
modifying activity on select proteins, which has an existing investigational new drug application (“IND”). We are in
the process of initiating discussions with the U.S. FDA to launch a future phase II clinical trial for LP-300 with a stratified
patient population of approximately 40 to 75 patients. Our new drug candidate, LP-184, is in a preclinical translational ex
vivo study using fresh human biopsies. LP-184 is a next generation alkylating agent with nanomolar potency that preferentially
damages DNA in cancer cells that overexpress certain biomarkers. LP-184 is in the fulvene class of compounds and has shown preliminary
preclinical indications of lower toxicity, longer half-life, and increased antitumor activity as compared to other compounds in
this drug class. Subject to regulatory clearance to move forward under a future IND application, we are planning a Phase I clinical
trial for LP-184 across multiple solid tumors that express a certain biomarker profile, and in glioblastoma to begin in late 2021
or early 2022. Our antibody drug conjugate (ADC) program is in early stage development and compound optimization for solid tumors.
LP-100
(Irofulven) is showing promise in solid tumors, primarily prostate cancer, where it is being advanced in an out-licensing transaction
with Allarity Therapeutics, after being in-licensed and developed by us. LP-100 has been well-tolerated, based on initial
observations from a phase II clinical trial in Europe in mCRPC. Continuing enrollment for this Phase II clinical trial has slowed
during the COVID-19 pandemic. Allarity Therapeutics has also stated that it is focusing its existing resources on other programs
that are currently higher priority for Allarity than LP-100. As of the date of this report, we are unable to forecast the timeline
for the completion of the Phase II clinical trial. Recently published data (also supported by prior publications on Irofulven)
indicates that tumors carrying mutations in ERCC2 and ERCC3 genes are likely to be sensitive to LP-100, and that the drug will
be synthetically lethal in these tumors, in a fashion similar to the activity of PARP inhibitors in BRCA deficient tumors. These
observations expand the potential treatment indications for LP-100 to include urothelial tumors, including bladder cancers, since
as many as 10% of bladder cancers carry ERCC2/ERCC3 mutations. These indications may represent a more rapid and efficient path
to potential approval of LP-100, and we are evaluating possibilities aimed at maximizing the value of these additional observations.
2
Most
patients with metastatic prostate cancer present with localized cancer, for which the standard of care may include active observation,
radiation, surgery, and androgen deprivation/suppression therapy. Responses to such therapy can be transient and many patients
will develop a castration resistant prostate cancer (CRPC) and develop, or are at risk to develop, mCRPC which accumulates genomic
alterations including DNA repair deficits. Chemotherapeutic agents play a critical role in the management of both metastatic castration
sensitive and mCRPC. The frequent use of the chemotherapy drug docetaxel in treating metastatic androgen sensitive prostate cancers
exemplifies this role. Historical observations of potential anticancer activity of LP-100 in clinical studies with prostate cancer,
and evidence of sensitivity to LP-184 in prostate cancer cell lines along with the development of computational methods that integrate
gene expression signatures, support LP-184 as a drug candidate with potential for use in combination with androgen deprivation
therapy for metastatic prostate cancer that is castration sensitive as well as metastatic prostate cancer that is castration resistant.
LP-184
is a new small molecule drug candidate that in preliminary preclinical studies has demonstrated increased plasma stability, reduced
total body clearance, significantly longer half-life, and potentially greater tumor regression than other studied fulvene based
compounds. We estimate that a substantial number of patients each year who suffer from metastatic prostate cancer globally could
be eligible for potential treatment with LP-184, if approved. In addition, the observed nanomolar potency of LP-184 suggests that
it may have anticancer properties in a wide range of solid tumors as an alkylating agent that works by causing DNA damage in tumor
cells. Other indications for LP-184 in solid tumors are emerging as a result of early developmental and biomarker studies, including
ovarian, breast, liver, kidney, pancreatic and thyroid cancers, as well as certain glioblastomas.
Further
work on these biomarkers both in-silico and in preclinical studies will help to establish a genomic signature that may
accelerate our time to a clinical trial and help guide patient selection. We believe that the market for LP-184 as a molecularly-targeted
drug candidate could be significant.
LP-300
(disodium 2,2’-dithio-bis-ethane sulfonate or dimesna) is a late-stage clinical drug candidate that was in-licensed by us
from BioNumerik Pharmaceuticals, Inc. (“BioNumerik”) in May 2016, and subsequently acquired by us in January of 2018.
Using our RADR®platform as part of the drug rescue process, we have identified LP-300 for use in a more targeted
set of cancer patients who exhibit a biomarker profile that we believe correlates with non-or never smoking status but still have
a form of non-small cell lung cancer (NSCLC). LP-300, originally branded as Tavocept®, is a molecular entity that
we believe may be capable of ameliorating the toxic side effects of chemotherapeutic drugs such as cisplatin, and it also appears
to act as a potential chemoenhancer. LP-300 has been studied in multiple randomized, controlled, multi-center non-small cell lung
cancer (NSCLC) trials that included administration of either paclitaxel and cisplatin and/or docetaxel and cisplatin. Since acquiring
LP-300 from BioNumerik, we have not yet conducted further clinical testing of LP-300. We are currently evaluating LP-300 for the
launch of a targeted phase II trial, in non or never smoking patients with NSCLC in combination with chemotherapy, under an existing
IND.
3
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.
Retrospective
analyses of the results of a multi-country phase III lung cancer trial conducted by BioNumerik in subgroups of NSCLC 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. We plan on advancing this drug candidate
for the never or non-smoker population of patients due to the following important market and clinical need factors:
We
are focused on advancing the development of LP-300 as a combination therapy for non or never-smokers with NSCLC adenocarcinoma
and potentially among non or never-smokers with a genomic signature that correlates with a higher potential of response to this
drug compound. We selected NSCLC in non- or never smokers as our lead proposed indication because it is a cancer with a growing
patient population, without effective treatment options, and LP-300 has shown an improvement in overall survival in this targeted
sub-group population in prior clinical studies.
4
In
vitro studies indicate that the target-specific effects of LP-300 potentially correlate to the covalent modification of accessible
cysteine residues important in protein function/structure. These could be involved in disruption/ blocking of cofactor binding
sites resulting in blocking of oncoproteins such as ALK, MET, ROS1, and EGFR that are more commonly altered in female non-smokers
than in any other group. Other potential mechanisms of action of LP-300 could include impact on stress induced oxidoreductases
thereby allowing LP-300 to exert its potential chemo-enhancing effects in the presence of chemotherapeutic agents such as cisplatin.
LP-300 is postulated to potentiate antitumor cytotoxicity of standard of care chemotherapy agents such as cisplatin. We believe
a key LP-300 related mechanism is likely to occur through the increase of tumor cell sensitivity to oxidative stress. Additionally,
via induction of NRF2 (also known as NFE2L2), LP-300 has the potential to provide protection of healthy cells against chemotherapy-associated
toxicity, and such protection potential was observed with LP-300 combination therapy in both prior nonclinical studies and clinical
trials
A
differential gene expression analysis of whole transcriptome profiling data from LP-300 treated versus untreated NSCLC adenocarcinoma
cells has been performed. Using a threshold of fold change > 2 out of a set of 51 curated NRF2 (NFE2L2) target genes as well
as NRF2 itself, we observed the top significantly upregulated genes in response to LP-300 exposure. Based on our observations,
we believe these genes could include NFE2L2, NQO1, PHGDH, HMOX1, SLC7A11, SRXN1, SOX2, GPX2, GPX3, GPX4, GPX7, G6PD, SIRT1, ITGB2
and BCL2. Our analysis indicates that these genes preferentially map to the following biological signaling pathways: (i) detoxification
of reactive oxygen species; (ii) glutathione metabolism; and (iii) inflammatory response. We filed a patent application in March
of 2020 on this discovery.
The
interaction network of selected genes along with the associated biological pathways is shown in the figure below.
5
As
part of our overall growth strategy, we plan to grow our pipeline by identifying new drug candidates and pursuing potential indications
for both LP-184 and LP-300 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 intend to select our next clinical program in
the next twelve months.
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 2021, we own or control over 70 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 implement an
efficient and thorough workflow 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 between one to three years 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.
6
Our
current pipeline of development programs involves three small molecule drug candidates: LP-100, LP-184 and LP-300.
LP-100
is currently being advanced by our licensee, Allarity Therapeutics. LP-184 and LP-300 are being advanced solely by us. There is
currently no active IND in the U.S. for LP-100 and LP-184. 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.
Additional
Portfolio Opportunities
Based
on the recognition of antibody drug conjugates (ADCs) as a promising therapeutic approach for cancer treatment, and one that has
growing interest due to the potential to increase targeted cancer cell death, we have started reviewing our portfolio of small
molecules for their potential to be used as part of an ADC approach. ADCs can increase selectivity and maximize tumor cell death
and also minimize collateral toxicity. Our compounds LP-100 and LP-184 have the potential to be linked to antibodies (or peptides)
and used in potential additional indications or alongside other small molecules or immuno-oncology agents. We believe that LP-300
can also play a role in developing ADC constructs and increasing the potential to deliver targeted therapies. We are actively
researching and reviewing potential development pathways and partnerships that would enable us to develop an ADC complement to
our portfolio.
The
last two years have seen five FDA approvals in the growing class of ADCs for therapeutic use. This has driven increased deal-making
and portfolio additions by large pharma companies. In addition to the acquisition of Immunomedics by Gilead, Merck acquired Velos
Bio in November of 2020 and NBE Therapeutics was acquired by Boehringer Ingelheim in December of 2020. It is notable that both
NBE and Velos, at the time of their acquisition, had just successfully completed Phase 1 trials using their ADC approach in specific
cancer subtypes.
7
On
December 30, 2020, we entered into an Evaluation and Limited Use Agreement (the “Evaluation Agreement”) with Califia
Pharma, Inc. (“Califia”). Califia’s founder, Michael J. Kelner, M.D., is a widely published researcher with
recognized expertise in the areas of illudofulvene chemistry and antibody drug conjugates. Califia has developed novel transcriptional-coupled
repair inhibitors that have demonstrated potential for an improved therapeutic index compared to traditional ADC payloads.
The
Evaluation Agreement provides for Lantern and Califia to collaborate on the in vitro and in vivo testing and evaluation of novel
Califia payloads conjugated to a Lantern targeting entity. The Evaluation Agreement also provides us with the right to negotiate
with Califia for exclusive license rights to use LP-184 and related analogs as the payload with an affinity drug conjugate or
small molecule drug conjugate targeting entity supplied by Lantern. We also have the right under the Evaluation Agreement to negotiate
for non-exclusive license rights to use a Lantern targeting entity with a payload and linker combination selected from novel specified
Califia payloads and linkers.
We
plan on increasing our focus on CNS (Central Nervous System) cancers based on the promising data that has been generated in experiments
conducted with LP-184. LP-184 has shown that it can cross the blood brain barrier (BBB) while leaving neuronal cells intact. This
unique profile has been validated in neuronal cell-plate assays, neuronal spheroids, and also in xenograft models and is now undergoing
further validation in a collaboration with an affiliate of the Johns
Hopkins School of Medicine. We have launched a program in GBM, and have uncovered several
additional CNS cancers we believe will be sensitive to LP-184 based on genomic profiling and biomarker analysis conducted with
our A.I. platform, RADR. We expect to focus additional resources on developing LP-184 as both monotherapy and combination therapy
in several rare and ultra-rate CNS and brain cancers.
Based
on the positive data regarding the blood brain barrier permeability for LP-184, we reviewed and analyzed a range of CNS cancers,
beyond GBM, that we believe to have the potential to be responsive to LP-184 and make an improvement in patient survival. One
of the CNS tumor types that was identified by RADR was ATRT, Atypical Teratoid Rhabdoid Tumor, which is a very rare, fast-growing
tumor of the brain and spinal cord. It usually occurs in children aged three years and younger, but can also occur in older children
and sometimes adults. However, the younger the patient the poorer the prognosis for survival. ATRT has no known approved targeted
therapies and there is no standard chemotherapy regimen. According to the NCI, approximately 90 percent of ATRTs are characterized
by a SMARCB1 mutation, which significantly reduces the ability of the surrounding cells to suppress the tumor. ATRT occurs in
about 50 to 60 children per year and less than 10 adults per year, although recent diagnosis has increased due to improved access
to cancer care and improved diagnostic methods. We believe that we can target this genetically defined subset of ultra-rare ATRT
cancers, and plan on pursuing this indication in collaborations with academic cancer centers and potentially pursuing orphan or
fast-track status if the additional data we obtain supports that this has the potential for changing the clinical outcome for
patients.
We
have obtained initial cell line data regarding LP-184 and ATRT that we believe supports the potential for LP-184 to qualify in
the future for possible grant of a Rare Pediatric Disease Designation for use of LP-184 for ATRT. Additionally, we believe that
subject to LP-184 successfully completing required clinical trials and regulatory requirements, LP-184 for the rare disease indication
of ATRT may also potentially qualify for grant of a Rare Pediatric Disease Priority Review Voucher ("PRV"). Under Section
529 of the Federal Food, Drug, and Cosmetic Act, FDA will award a PRV to sponsors of rare pediatric disease product applications
that meet certain criteria. Under this program, a sponsor that receives approval for a drug or biologic for a rare pediatric disease
may qualify for a PRV that can be redeemed to receive expedited review of a subsequent product marketing application. A PRV
may be redeemed by the company that initially receives it, or the PRV can be sold to another
company.
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.
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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
will drive 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 preclinical “PRostate cancer Artificial Intelligence Study using Ex
vivo models” or “PRAISE” trial and our planned clinical trial for LP-184 are intended to examine biomarkers
related to LP-184’s molecular and cellular targets to identify those that may correlate with clinical observed anticancer
activity. 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 female non- or never smokers than in any other group 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 plan is to bring LP-300 into a targeted phase 2 clinical trial within the non- or never-smoker
sub-group that are identified with the adenocarcinoma sub-type of NSCLC.
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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.
RADR®
Platform Architecture and Modules
Our
platform uses AI and machine learning to identify genes and genomic signatures believed to be highly correlated with drug sensitivity.
These statistically significant genes are furthered filtered in the pathway network and interaction analysis to identify genes
believed to be biologically relevant. Genes that make up this layer are either related to the molecule’s mechanism of action
or heavily connected to each other in gene networks. Lastly, another inductive learning algorithm ranks these filtered genes based
on drug sensitivity by calculating the half maximal inhibiting concentration (IC50) of the correlated relationship.
In this way, our platform has the potential to predict drug sensitivity, classify a patient as responder or non-responder and
identify biomarkers for each drug-tumor combination.
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.
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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. 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.
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 1.2 billion oncology-specific clinical and preclinical data points, more than 140 drug-cancer interactions,
and over 55,000 patient records from five data bases, one of which is our internal data base. Our long-term objective is to collect
and analyze over ten 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%.
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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.
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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.