Oncotelic Highlights PDAOAI Discovery Platform and HCC Research at Drug Discovery 2026
- Scott Myers described how PDAOAI searches and organizes an index of approximately 29 million PubMed abstracts to support focused biomedical research.
- Saran Saund presented a four-gene HCC signature associated with survival in an exploratory discovery analysis and an independent Japanese cohort.
- BIO REBOOT connects the platform’s research capabilities with potential funding and development support for selected projects.
Oncotelic Therapeutics Inc. (OTCQB: OTLC) (“Oncotelic”) is pursuing a development strategy that connects biomedical literature analysis with the practical work of developing therapeutics. At Drug Discovery 2026 in Burlingame, California, Scott Myers and Saran Saund showed how the company’s PDAOAI platform can help researchers organize scientific evidence and develop hypotheses for further investigation.
Myers, Oncotelic’s Product Manager, presented “The Infinity Problem: AI Search Aggregation for Biomedical Knowledge Discovery in Drug Development” on October 7. Saund, Chief Business Officer, followed on October 8 with an HCC case study describing how disease-specific literature analysis led to a four-gene mTOR-related signature associated with patient survival.
For investors, the presentations connect Oncotelic’s discovery tools with its drug-development ambitions. They also clarify the next scientific question: whether a signature associated with prognosis can help identify differences in treatment benefit.
Making a growing literature corpus usable
PDAOAI’s current index contains approximately 29 million PubMed abstracts. Myers addressed a practical challenge: selecting useful evidence from a growing literature base when an AI model can process only a finite amount of information at once.
The workflow combines semantic and keyword search with PubMed filters and reranking. Researchers can search the broader index or supply selected PubMed identifiers and abstracts. Shell sampling explores related ideas beyond the closest matches while keeping the research question in focus.
Partitioned Literature Aggregation, or PLA, analyzes defined groups of literature separately using the same question, then combines the summaries. This gives each group an explicit assessment while preserving each group’s context. Researchers review source papers, assess conflicting findings, and refine questions when evidence is incomplete. Some questions require examination of full papers beyond their abstracts.
An HCC case study with a reproducible survival signal
Saund’s presentation applied that approach to a historical study covering 4.4 million abstract records across 24 overlapping disease corpora. That study scope is distinct from PDAOAI’s current approximately 29 million-abstract index. Assessing each disease separately aimed to reduce the risk that differences in publication volume would crowd less-published diseases out of a limited evidence window. Evidence quality still matters.
The HCC investigation began with an important negative clinical result. As Saund reported, the EVOLVE-1 trial in 546 patients with advanced HCC after sorafenib found no overall survival benefit from everolimus: median survival was 7.6 months versus 7.3 months with placebo, with a hazard ratio of 1.05 and P = 0.68. That outcome prompted a closer examination of whether mTOR-related biology could distinguish patient groups.
The literature work generated candidate genes, and subsequent survival analysis narrowed a 12-gene set to FKBP1A, EIF4E, RRAGC, and PPARGC1A. The adverse score adds standardized expression values for the first three genes and subtracts the standardized expression value for PPARGC1A. Higher scores were associated with poorer survival.
In an exploratory KM Plotter discovery analysis showing 364 patients at time zero, the high adverse-score group had an overall survival hazard ratio of 2.59 relative to the low-score group (95% confidence interval 1.80–3.74; log-rank P = 1.1 × 10⁻⁷). Median survival was 37.8 months in the high-score group and 84.4 months in the low-score group. Saund identified this as an outcome-informed discovery analysis.
The survival association reproduced in an independent Japanese cohort of 203 patients from ICGC LIRI-JP/HCCDB18, using the same four genes and scoring directions. According to the presentation, the association remained after accounting for age, sex, and cancer stage. The research was described in a submitted manuscript cited in the slides.
These findings support further research into the signature as a prognostic marker. They do not yet establish that it predicts benefit from everolimus, the investigational Sapu003 formulation developed through Oncotelic’s joint venture, or another mTOR-directed therapy. Saund outlined subsequent work involving a consistent gene test and fixed scoring formula, laboratory response studies, and controlled patient studies comparing treatment benefit across gene patterns.
Connecting research hypotheses with development capabilities
The HCC findings also provide a rationale for further research related to Sapu003, Oncotelic’s joint venture’s investigational intravenous formulation of everolimus developed using its Deciparticle(TM) technology. PDAOAI helps identify and assess biological hypotheses; formulation capabilities address how a potential treatment could be delivered. Establishing clinical value requires further experimental and clinical testing.
Saund also discussed literature-derived hypotheses involving YAP1 in taxane resistance and FOXO3 in aging and cellular maintenance. These examples broaden the platform’s potential research applications, with experimental validation still required.
Both presenters highlighted BIO REBOOT, which invites scientists, AI developers, biotech founders, individuals, and teams to bring research questions and therapeutic concepts to PDAOAI. Selected projects may qualify for startup funding and development support involving computational chemistry, biological validation, formulation, regulatory planning, and clinical development. Support depends on scientific review, available resources, and agreed terms.
The conference presentations give investors a clearer view of how Oncotelic intends to connect its research platform with drug-development opportunities. Scott’s talk explained the discovery workflow; Saran’s HCC case study demonstrated a specific hypothesis with a survival association reproduced in a separate cohort. The next step is to test whether the signature can guide treatment decisions and inform drug development.
BIO REBOOT registration is available at https://pdaoai.com/bio-reboot.
For more information, visit the company’s website at www.Oncotelic.com.
NOTE TO INVESTORS: The latest news and updates relating to OTLC are available in the company’s newsroom at https://nnw.fm/OTLC
Please see full terms of use and disclaimers on the NetworkNewsWire website applicable to all content provided by NNW, wherever published or republished: http://NNW.fm/Disclaimer
- Scott Myers described how PDAOAI searches and organizes an index of approximately 29 million PubMed abstracts to support focused biomedical research.
- Saran Saund presented a four-gene HCC signature associated with survival in an exploratory discovery analysis and an independent Japanese cohort.
- BIO REBOOT connects the platform’s research capabilities with potential funding and development support for selected projects.
Oncotelic Therapeutics Inc. (OTCQB: OTLC) (“Oncotelic”) is pursuing a development strategy that connects biomedical literature analysis with the practical work of developing therapeutics. At Drug Discovery 2026 in Burlingame, California, Scott Myers and Saran Saund showed how the company’s PDAOAI platform can help researchers organize scientific evidence and develop hypotheses for further investigation.
Myers, Oncotelic’s Product Manager, presented “The Infinity Problem: AI Search Aggregation for Biomedical Knowledge Discovery in Drug Development” on October 7. Saund, Chief Business Officer, followed on October 8 with an HCC case study describing how disease-specific literature analysis led to a four-gene mTOR-related signature associated with patient survival.
For investors, the presentations connect Oncotelic’s discovery tools with its drug-development ambitions. They also clarify the next scientific question: whether a signature associated with prognosis can help identify differences in treatment benefit.
Making a growing literature corpus usable
PDAOAI’s current index contains approximately 29 million PubMed abstracts. Myers addressed a practical challenge: selecting useful evidence from a growing literature base when an AI model can process only a finite amount of information at once.
The workflow combines semantic and keyword search with PubMed filters and reranking. Researchers can search the broader index or supply selected PubMed identifiers and abstracts. Shell sampling explores related ideas beyond the closest matches while keeping the research question in focus.
Partitioned Literature Aggregation, or PLA, analyzes defined groups of literature separately using the same question, then combines the summaries. This gives each group an explicit assessment while preserving each group’s context. Researchers review source papers, assess conflicting findings, and refine questions when evidence is incomplete. Some questions require examination of full papers beyond their abstracts.
An HCC case study with a reproducible survival signal
Saund’s presentation applied that approach to a historical study covering 4.4 million abstract records across 24 overlapping disease corpora. That study scope is distinct from PDAOAI’s current approximately 29 million-abstract index. Assessing each disease separately aimed to reduce the risk that differences in publication volume would crowd less-published diseases out of a limited evidence window. Evidence quality still matters.
The HCC investigation began with an important negative clinical result. As Saund reported, the EVOLVE-1 trial in 546 patients with advanced HCC after sorafenib found no overall survival benefit from everolimus: median survival was 7.6 months versus 7.3 months with placebo, with a hazard ratio of 1.05 and P = 0.68. That outcome prompted a closer examination of whether mTOR-related biology could distinguish patient groups.
The literature work generated candidate genes, and subsequent survival analysis narrowed a 12-gene set to FKBP1A, EIF4E, RRAGC, and PPARGC1A. The adverse score adds standardized expression values for the first three genes and subtracts the standardized expression value for PPARGC1A. Higher scores were associated with poorer survival.
In an exploratory KM Plotter discovery analysis showing 364 patients at time zero, the high adverse-score group had an overall survival hazard ratio of 2.59 relative to the low-score group (95% confidence interval 1.80–3.74; log-rank P = 1.1 × 10⁻⁷). Median survival was 37.8 months in the high-score group and 84.4 months in the low-score group. Saund identified this as an outcome-informed discovery analysis.
The survival association reproduced in an independent Japanese cohort of 203 patients from ICGC LIRI-JP/HCCDB18, using the same four genes and scoring directions. According to the presentation, the association remained after accounting for age, sex, and cancer stage. The research was described in a submitted manuscript cited in the slides.
These findings support further research into the signature as a prognostic marker. They do not yet establish that it predicts benefit from everolimus, the investigational Sapu003 formulation developed through Oncotelic’s joint venture, or another mTOR-directed therapy. Saund outlined subsequent work involving a consistent gene test and fixed scoring formula, laboratory response studies, and controlled patient studies comparing treatment benefit across gene patterns.
Connecting research hypotheses with development capabilities
The HCC findings also provide a rationale for further research related to Sapu003, Oncotelic’s joint venture’s investigational intravenous formulation of everolimus developed using its Deciparticle(TM) technology. PDAOAI helps identify and assess biological hypotheses; formulation capabilities address how a potential treatment could be delivered. Establishing clinical value requires further experimental and clinical testing.
Saund also discussed literature-derived hypotheses involving YAP1 in taxane resistance and FOXO3 in aging and cellular maintenance. These examples broaden the platform’s potential research applications, with experimental validation still required.
Both presenters highlighted BIO REBOOT, which invites scientists, AI developers, biotech founders, individuals, and teams to bring research questions and therapeutic concepts to PDAOAI. Selected projects may qualify for startup funding and development support involving computational chemistry, biological validation, formulation, regulatory planning, and clinical development. Support depends on scientific review, available resources, and agreed terms.
The conference presentations give investors a clearer view of how Oncotelic intends to connect its research platform with drug-development opportunities. Scott’s talk explained the discovery workflow; Saran’s HCC case study demonstrated a specific hypothesis with a survival association reproduced in a separate cohort. The next step is to test whether the signature can guide treatment decisions and inform drug development.
BIO REBOOT registration is available at https://pdaoai.com/bio-reboot.
For more information, visit the company’s website at www.Oncotelic.com.
NOTE TO INVESTORS: The latest news and updates relating to OTLC are available in the company’s newsroom at https://nnw.fm/OTLC
Please see full terms of use and disclaimers on the NetworkNewsWire website applicable to all content provided by NNW, wherever published or republished: http://NNW.fm/Disclaimer
