Lantern Pharma Unveils Innovative AI-Powered Module to Improve the Precision, Cost and Timelines of Antibody-Drug Conjugate (ADC) Development for Cancer
Lantern Pharma (NASDAQ: LTRN) has announced advancements in its RADR® AI platform for optimizing antibody-drug conjugate (ADC) development. The company's AI-driven approach has successfully identified 82 promising ADC targets and 290 target-indication combinations, while validating 729 potential payload molecules from over 50,000 compounds.
The research demonstrated that 22 targets have been clinically validated, while 60 novel targets present new intellectual property opportunities. The platform's payload molecules showed exceptional potency with GI50 values from picomolar to 10 nM ranges. According to the company, this AI-driven approach could reduce ADC development timelines by 30-50% and cut costs by up to 60% compared to traditional methods.
The global ADC market is projected to reach $30.4 billion by 2028, growing at a CAGR of 41.7%. The company is advancing multiple ADC candidates through preclinical development, including a collaboration with the MAGICBULLET::Reloaded Initiative at the University of Bielefeld.
Lantern Pharma (NASDAQ: LTRN) ha annunciato progressi nella sua piattaforma di intelligenza artificiale RADR® per ottimizzare lo sviluppo di coniugati farmaco-anticorpo (ADC). L'approccio basato sull'AI della società ha identificato con successo 82 potenziali target ADC e 290 combinazioni target-indicazione, validando nel contempo 729 possibili molecole payload da oltre 50.000 composti.
La ricerca ha dimostrato che 22 target sono stati validati clinicamente, mentre 60 target nuovi offrono nuove opportunità di proprietà intellettuale. Le molecole payload della piattaforma hanno mostrato una potenza eccezionale con valori GI50 che variano da picomolari a 10 nM. Secondo l'azienda, questo approccio guidato dall'AI potrebbe ridurre i tempi di sviluppo degli ADC del 30-50% e abbattere i costi fino al 60% rispetto ai metodi tradizionali.
Il mercato globale degli ADC è previsto raggiungere 30,4 miliardi di dollari entro il 2028, crescendo a un tasso di crescita annuale composto (CAGR) del 41,7%. L'azienda sta avanzando diversi candidati ADC attraverso lo sviluppo preclinico, inclusa una collaborazione con l'iniziativa MAGICBULLET::Reloaded presso l'Università di Bielefeld.
Lantern Pharma (NASDAQ: LTRN) ha anunciado avances en su plataforma de inteligencia artificial RADR® para la optimización del desarrollo de conjugados anticuerpo-fármaco (ADC). El enfoque impulsado por IA de la compañía ha identificado con éxito 82 objetivos prometedores de ADC y 290 combinaciones de objetivo-indicación, al mismo tiempo que validó 729 moléculas de carga potencial de más de 50,000 compuestos.
La investigación demostró que 22 objetivos han sido validados clínicamente, mientras que 60 nuevos objetivos presentan nuevas oportunidades de propiedad intelectual. Las moléculas de carga de la plataforma mostraron una potencia excepcional con valores GI50 que van desde picomolar hasta 10 nM. Según la empresa, este enfoque impulsado por IA podría reducir los plazos de desarrollo de ADC en un 30-50% y reducir los costos hasta en un 60% en comparación con los métodos tradicionales.
Se prevé que el mercado global de ADC alcance 30.4 mil millones de dólares para 2028, creciendo a un CAGR del 41.7%. La compañía está avanzando en múltiples candidatos de ADC a través del desarrollo preclínico, incluyendo una colaboración con la Iniciativa MAGICBULLET::Reloaded en la Universidad de Bielefeld.
랜턴 제약 (NASDAQ: LTRN)은 항체-약물 접합체 (ADC) 개발 최적화를 위한 RADR® AI 플랫폼의 발전을 발표했습니다. 이 회사의 AI 기반 접근법은 82개의 유망한 ADC 타겟과 290개의 타겟-지표 조합을 성공적으로 찾았으며, 50,000개 이상의 화합물에서 729개의 잠재적인 페이로드 분자를 검증했습니다.
연구 결과 22개의 타겟이 임상적으로 검증되었고, 60개의 새로운 타겟이 새로운 지식 재산권 기회를 제공합니다. 플랫폼의 페이로드 분자는 picomolar에서 10 nM 범위까지 뛰어난 효능을 보였습니다. 회사에 따르면, 이 AI 기반 접근법은 기존 방법보다 ADC 개발 소요 시간을 30-50% 줄이고 비용을 60%까지 절감할 수 있을 것이라고 합니다.
글로벌 ADC 시장은 2028년까지 304억 달러에 이를 것으로 예상되며, 연평균 성장률(CAGR)은 41.7%입니다. 이 회사는 비엘레펠트 대학교의 MAGICBULLET::Reloaded 이니셔티브와의 협력을 포함해 여러 ADC 후보를 전임상 개발로 진행하고 있습니다.
Lantern Pharma (NASDAQ: LTRN) a annoncé des avancées dans sa plateforme d'IA RADR® pour optimiser le développement des conjugués anticorps-médicaments (ADC). L'approche basée sur l'IA de la société a réussi à identifier 82 cibles prometteuses d'ADC et 290 combinaisons cible-indication, tout en validant 729 molécules de charge potentielles parmi plus de 50 000 composés.
La recherche a démontré que 22 cibles ont été validées cliniquement, tandis que 60 nouvelles cibles présentent de nouvelles opportunités de propriété intellectuelle. Les molécules de charge de la plateforme ont montré une puissance exceptionnelle avec des valeurs GI50 allant de la picomolaire à 10 nM. Selon l'entreprise, cette approche alimentée par l'IA pourrait réduire les délais de développement des ADC de 30 à 50 % et diminuer les coûts jusqu'à 60 % par rapport aux méthodes traditionnelles.
Le marché mondial des ADC devrait atteindre 30,4 milliards de dollars d'ici 2028, avec une croissance à un taux de croissance annuel composé (CAGR) de 41,7 %. L'entreprise fait progresser plusieurs candidats ADC à travers le développement préclinique, y compris une collaboration avec l'initiative MAGICBULLET::Reloaded à l'Université de Bielefeld.
Lantern Pharma (NASDAQ: LTRN) hat Fortschritte bei seiner RADR® KI-Plattform zur Optimierung der Entwicklung von Antikörper-Wirkstoff-Konjugaten (ADC) bekannt gegeben. Der KI-gesteuerte Ansatz des Unternehmens hat erfolgreich 82 vielversprechende ADC-Ziele und 290 Ziel-Indikations-Kombinationen identifiziert, während 729 potenzielle Payload-Moleküle aus über 50.000 Verbindungen validiert wurden.
Die Forschung zeigte, dass 22 Ziele klinisch validiert wurden, während 60 neuartige Ziele neue Möglichkeiten zur geistigen Eigentum bieten. Die Payload-Moleküle der Plattform zeigten eine außergewöhnliche Potenz mit GI50-Werten im Bereich von Pikomol bis 10 nM. Laut dem Unternehmen könnte dieser KI-gesteuerte Ansatz die Entwicklungszeiten für ADC um 30-50% reduzieren und die Kosten um bis zu 60% im Vergleich zu traditionellen Methoden senken.
Der globale ADC-Markt wird voraussichtlich bis 2028 30,4 Milliarden Dollar erreichen und mit einer jährlichen Wachstumsrate (CAGR) von 41,7% wachsen. Das Unternehmen bringt mehrere ADC-Kandidaten in die präklinische Entwicklung, darunter eine Zusammenarbeit mit der MAGICBULLET::Reloaded-Initiative an der Universität Bielefeld.
- AI platform successfully identified 82 ADC targets and 290 target-indication combinations
- Potential to reduce development timelines by 30-50% and costs by up to 60%
- 22 targets already validated in clinical/preclinical settings
- 60 novel targets represent new IP and licensing opportunities
- Operating in rapidly growing ADC market projected to reach $30.4B by 2028 (41.7% CAGR)
- ADC candidates still in preclinical stage, indicating long path to commercialization
Insights
Lantern Pharma's new AI-powered ADC module represents a potential game-changer in the rapidly growing ADC market, projected to reach
The platform's demonstrated capability in identifying ultra-potent payload molecules with picomolar to 10 nM potency ranges positions Lantern competitively in a market where recent ADC-focused acquisitions have exceeded
Three key value drivers emerge from this announcement:
- The potential
60% cost reduction and 30-50% timeline acceleration could significantly improve ROI for ADC development programs - The validation of 22 existing targets demonstrates the platform's accuracy, while 60 novel targets create multiple revenue opportunities through licensing and partnerships
- The machine-learning ready approach provides a scalable solution for systematic evaluation of thousands of tumor subtypes, creating a sustainable competitive advantage
The collaboration with the MAGICBULLET::Reloaded Initiative adds credibility and provides an established pathway for preclinical validation. This positions Lantern favorably for potential partnerships with major pharmaceutical companies seeking to accelerate their ADC programs or expand their oncology pipelines.
In a peer-reviewed study published in PLOS ONE, Lantern Pharma researchers demonstrated how their AI-driven approach successfully identified 82 promising ADC targets and 290 target-indication combinations, while also validating 729 potential payload molecules from a screening of over 50,000 compounds. Notably, 22 of these targets have already been validated in clinical or preclinical settings, demonstrating the platform's ability to identify clinically relevant targets. The remaining 60 novel targets represent significant potential for new intellectual property, portfolio development of ADC candidates at Lantern Pharma and licensing opportunities with other biotech and pharma companies. The ADC module helped to characterize payload molecules with exceptional potency, exhibiting GI50 values from picomolar to 10 nM (nanomolar) ranges. These payload molecules can be further optimized by leveraging RADR’s comprehensive molecular features database by mapping drug-response relationships with biochemical and molecular structure characteristics. This AI-driven optimization capability could potentially enhance both the selective targeting and therapeutic window of these ADC payload candidates. Lantern Pharma continues to advance the methods and automations outlined in the paper as part of it’s RADR™ AI platform and further enhance the data and computational precision of the module.
“This breakthrough demonstrates how AI can transform the traditionally costly and time-consuming process of ADC development," said Panna Sharma, CEO & President of Lantern Pharma. "By simultaneously analyzing multiple data types and integrating mutation profiles with target expression, our team was able to identify optimal therapeutic combinations that have the potential to be more effective and safer for specific patient populations. We believe that our data-driven and machine-learning ready approach could reduce ADC development timelines by 30 to
The research leverages Lantern's proprietary RADR® platform to analyze complex datasets including transcriptomics, proteomics, and mutation profiles across 22 tumor types. The platform's ability to predict mutation-specific responses could enable more precise patient stratification in clinical trials, potentially increasing success rates and reducing development costs. This precision approach to ADC development could be valuable for biotech and pharmaceutical companies looking to advance their ADC portfolio in more targeted indications and is also being actively used by Lantern in the development and modeling of their ADC candidates in preclinical testing and optimization.
"The implications of this research extend far beyond just expanding the repertoire of potential ADC targets," said Kishor Bhatia, Ph.D., Chief Scientific Officer at Lantern Pharma. "By leveraging our RADR® platform's advanced AI capabilities, we've created a systematic approach that could dramatically reduce both the time and cost of ADC development while increasing the probability of clinical success. Our platform is particularly well-suited for partnership opportunities with pharmaceutical companies looking to accelerate their ADC programs or expand their pipeline with novel targets."
Key Highlights of the AI-powered ADC module include:
- Demonstrated platform validation through the successful identification of 22 clinically proven targets with established therapeutic potential
- Discovered 60 novel targets that present significant opportunities for new intellectual property development, portfolio expansion, and strategic licensing partnerships
- Developed proprietary mutation-specific targeting capabilities that enable improved clinical trial design, enhanced precision in indication selection, and more accurate patient response predictions
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Established a framework that could reduce ADC development costs by up to
60% and accelerate development timelines by 30-50% for both Lantern Pharma and its collaborators - Created a highly scalable, machine-learning ready approach that leverages the RADR™ AI platform to systematically evaluate thousands of potential tumor sub-types and indications
- Designed a clear pathway to commercialization through strategic industry partnerships and collaborative development programs
The complete research paper, titled "Expanding the repertoire of Antibody Drug Conjugate (ADC) targets with improved tumor selectivity and range of potent payloads through in-silico analysis," is available in PLOS ONE at https://doi.org/10.1371/journal.pone.0308604. The paper outlines the approach and initial data-sets used in the development of the AI-powered ADC development module which continues to be enhanced, and is being further validated by Lantern Pharma.
About Lantern Pharma
Lantern Pharma (NASDAQ: LTRN) is an AI company transforming the cost, pace, and timeline of oncology drug discovery and development. Our proprietary AI and machine learning (ML) platform, RADR®, leverages over 100 billion oncology-focused data points and a library of 200+ advanced ML algorithms to help solve billion-dollar, real-world problems in oncology drug development. By harnessing the power of AI and with input from world-class scientific advisors and collaborators, we have accelerated the development of our growing pipeline of therapies that span multiple cancer indications, including both solid tumors and blood cancers and an antibody-drug conjugate (ADC) program. Our lead development programs include a Phase 2 clinical program and multiple Phase 1 clinical trials. Our AI-driven pipeline of innovative product candidates is estimated to have a combined annual market potential of over
Please find more information at:
- Website: www.lanternpharma.com
- LinkedIn: https://www.linkedin.com/company/lanternpharma/
- X: @lanternpharma
FORWARD LOOKING STATEMENT:
This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These forward-looking statements include, among other things, statements relating to: future events or our future financial performance; the potential advantages of our RADR® platform in identifying drug candidates and patient populations that are likely to respond to a drug candidate; our strategic plans to advance the development of our drug candidates and antibody drug conjugate (ADC) development program; estimates regarding the development timing for our drug candidates and ADC development program; expectations and estimates regarding clinical trial timing and patient enrollment; our research and development efforts of our internal drug discovery programs and the utilization of our RADR® platform to streamline the drug development process; our intention to leverage artificial intelligence, machine learning and genomic data to streamline and transform the pace, risk and cost of oncology drug discovery and development and to identify patient populations that would likely respond to a drug candidate; estimates regarding patient populations, potential markets and potential market sizes; sales estimates for our drug candidates and our plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates ourselves or in collaboration with others. Any statements that are not statements of historical fact (including, without limitation, statements that use words such as "anticipate," "believe," "contemplate," "could," "estimate," "expect," "intend," "seek," "may," "might," "plan," "potential," "predict," "project," "target," “model,” "objective," "aim," "upcoming," "should," "will," "would," or the negative of these words or other similar expressions) should be considered forward-looking statements. There are a number of important factors that could cause our actual results to differ materially from those indicated by the forward-looking statements, such as (i) the risk that our research and the research of our collaborators may not be successful, (ii) the risk that observations in preclinical studies and early or preliminary observations in clinical studies do not ensure that later observations, studies and development will be consistent or successful, (iii) the risk that we may not be able to secure sufficient future funding when needed and as required to advance and support our existing and planned clinical trials and operations, (iv) the risk that we may not be successful in licensing potential candidates or in completing potential partnerships and collaborations, (v) the risk that none of our product candidates has received FDA marketing approval, and we may not be able to successfully initiate, conduct, or conclude clinical testing for or obtain marketing approval for our product candidates, (vi) the risk that no drug product based on our proprietary RADR® AI platform has received FDA marketing approval or otherwise been incorporated into a commercial product, and (vii) those other factors set forth in the Risk Factors section in our Annual Report on Form 10-K for the year ended December 31, 2023, filed with the Securities and Exchange Commission on March 18, 2024. You may access our Annual Report on Form 10-K for the year ended December 31, 2023 under the investor SEC filings tab of our website at www.lanternpharma.com or on the SEC's website at www.sec.gov. Given these risks and uncertainties, we can give no assurances that our forward-looking statements will prove to be accurate, or that any other results or events projected or contemplated by our forward-looking statements will in fact occur, and we caution investors not to place undue reliance on these statements. All forward-looking statements in this press release represent our judgment as of the date hereof, and, except as otherwise required by law, we disclaim any obligation to update any forward-looking statements to conform the statement to actual results or changes in our expectations.
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Source: Lantern Pharma Inc.
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