Oncotelic Therapeutics Inc · OTC:OTLC
Oncotelic Therapeutics — Drug Discovery 2026
13 slides from Oncotelic Therapeutics's conference presentation, Oct 13, 2026
Slide 1: Partitioning 4.4 Million Abstracts by Disease Turns a Literature Review into a Discovery
DRUG DISCOVERY 2026 Partitioning 4.4 million Abstracts by Disease Turns a Literature Review into a Discovery: A Four-Gene mTOR Signature Defining a Candidate Hepatocellular Carcinoma Population Saran Saund Chief Business Officer, Oncotelic Therapeutics October 8, 2026 Burlingame, California a1
Slide 2: Literature volume varies sharply by disease
Literature volume varies sharply by disease Abstract records in two of the study’s disease corpora Mantle cell lymphoma |7,979 0 250,000 500,000 750,000 More papers can crowd a limited evidence window. Publication volume can shape what reaches the final synthesis Chang et al., submitted manuscript v
Slide 3: Every disease gets one voice
Every disease gets one voice MONOLITHIC QUERY PARTITIONED LITERATURE AGGREGATION (PLA) ff ee 24 disease assessments, One monolithic synthesis then aggregate The same question for each disease. Evidence quality still matters. Chang et al., submitted manuscript v8. Methods 2.2 and Discussion. Conceptu
Slide 4: PubMed abstracts in vector space
29 million PubMed abstracts in vector space Biomedical literature Folding, clustering, sampling Source evidence Project biomedical text Analyze relationships Retrieve and review into vector space across the literature the source publications PDAOAI keeps the answer connected to its source publicatio
Slide 5: Partitioned Literature Aggregation
Partitioned Literature Aggregation The same clinical question, applied separately to every disease Retrieve Extract Resolve Partition Aggregate S Rapalog Disease Canonical Same question One assessment literature names disease terms in every corpus per disease 24 overlapping disease corpora. Each rec
Slide 6: EVOLVE-1 found no survival benefit in HCC
EVOLVE-1 found no survival benefit in HCC 2014: 546 patients with advanced HCC after sorafenib [A] Overall survival EVEROLIMUS VS PLACEBO HR 1.05 95% Cl 0.86—1.27 P=068 100 Median overall survival 7.6 vs 7.3 months 14 16 Time, mo No. at risk Everolimus 36: 259 207 163 125 105 92 70 9 Placebo 18: 3 1
Slide 7: The negative HCC result made us dig deeper
The negative HCC result made us dig deeper SAME QUESTION FOR EVERY CANCER “Have rapalogs shown clinical benefit in patients with this cancer?” PLA separates Compare Follow the PLA extracts Survival analysis by cancer clinical results HCC negative candidate genes narrows to four =. = ye Vey Ney ’ r h
Slide 8: The biology behind our four-gene signature
The biology behind our four-gene signature Four biological roles. One survival-associated signature. FKBP1A RRAGC EIF4E PPARGC1A Rapalog binding Nutrient signaling Protein production Metabolism and mitochondria S = z(FKBP1A) + z(EIF4E) + z(RRAGC) - z(PPARGC1A) Higher score was associated with poorer
Slide 9: Discovery cohort: four-gene survival separation
Discovery cohort: four-gene survival separation EXPLORATORY KM PLOTTER ANALYSIS HR = 2.59 (1.8 - 3.74) logrank P = 1.1e-07 > = @ 8 2 = Expression — low — high 0 20 40 60 Time (months) Number at risk low 1065332 high 182 6 1 10 5 Chang et al., submitted manuscript v8. Figure 3F, original non-ecitable
Slide 10: The survival signal held in a separate cohort
The survival signal held in a separate cohort ICGC LIRI-JP / HCCDB18: an independent Japanese patient cohort —Low adverse score +—High adverse score 100% 80% co = = 60% 5 a B oy & 40% g fo) 20% 0% 0 20 40 60 80 Months At risk / months oO 20 40 60 80 Low adverse 102 79 27 oO i) High adverse: 101 57 1
Slide 11: Prognosis and the next treatment test
Prognosis and the next treatment test OBSERVED PROPOSED TESTS OF TREATMENT BENEFIT Survival association 41 Use a consistent gene test . Measure the same four genes The four-gene signal reproduced with a fixed method and formula. in a separate patient cohort. 2 Test drug response in the lab 3. Test t
Slide 12: PDAOAI beyond HCC: YAP1 and FOXO3
PDAOAI beyond HCC: YAP1 and FOXO3 YAP1 FOXO3 Taxane resistance Aging and cellular maintenance Drug design and molecular docking Modeling FOXO3 release Literature-derived hypotheses. Defined experiments to test them. PDAOAI literature analyses, Candidate mechanisms require experimental validation. R
Slide 13: BIO REBOOT: Build the next drug company
PDAOAI / ONCOTELIC-SAPU BIO REBOOT Build the next drug company Find overlooked therapeutic opportunities with PDAOAI, an engine built on 29 million PubMed abstracts. SELECTED PROJECTS MAY QUALIFY FOR Startup funding Oncotelic-SAPU development support Computational chemistry, biological validation, f