Peter Oloche David

This repository contains the Python source code developed for the computational modeling, analysis, simulations, and visualizations associated with the research manuscript “Longitudinal ctDNA Constrains a Resistance-Evolution Model and Frames Counterfactual Adaptive Dosing in Neoadjuvantly Treated Breast Cancer.” The manuscript represents a substantially developed and revised stage of a research program previously disseminated under the title “Decoding Cancer Evolution: Computational Models for Tumor Dynamics and Precision Oncology” as an SSRN preprint (DOI: 10.2139/ssrn.6654018). An earlier abstract from this research program was peer reviewed and published as a conference abstract in Cancer Research, volume 85, Issue 23_Supplement (2025), under the title associated with AACR presentation B030 (DOI: 10.1158/1538-7445.CANEVOL25-B030). The present repository provides the computational code associated with the current manuscript and is deposited separately to support computational transparency, reproducibility, and independent examination of the methods and analyses.

Abstract Tumor heterogeneity poses a formidable challenge in oncology, manifesting as genetic, epigenetic, and phenotypic variations within neoplastic tissues that underpin therapeutic resistance and metastatic progression. This abstract delineates an integrative framework leveraging spatial transcriptomics and 3D chromatin conformation capture to unravel and mitigate intratumoral diversity. The primary objective is to dissect the spatiotemporal dynamics of genomic architecture in solid tumors, hypothesizing that aberrant chromatin looping and spatial gene expression patterns drive subclonal evolution. Employing high-resolution spatial RNA sequencing (e.g., Visium platform) alongside Hi-C and derivative techniques, we mapped 3D genomic interactions across patient-derived tumor organoids and biopsy samples from breast and colorectal carcinomas (n=45). Methodologically, we introduced a novel heterogeneity index, \( H = \sum_{i=1}^{N} \left(1 - \frac{\sum_{j=1}^{M} p_{ij} \log p_{ij}}{\log M}\right) \cdot w_i \), where \( p_{ij} \) denotes the probability of gene expression state j in spatial bin i, M represents expression states, and \( w_i \) weights topological domain integrity derived from Hi-C contact matrices. This metric quantifies entropy-adjusted spatial variance, revealing hotspots of genomic instability. Results demonstrated that 3D genomic reconfiguration, particularly enhancer-promoter hijacking in heterochromatic regions, correlates with elevated heterogeneity scores (r=0.72, p<0.001), fostering chemoresistance in 68% of subclones. Targeted CRISPR-mediated loop disruption reduced heterogeneity by 42% in vitro, as validated through single-cell ATAC-seq. In conclusion, this study pioneers a multidimensional genomics paradigm for decoding tumor complexity, offering actionable insights for precision therapeutics. By bridging spatial and structural layers, our approach paves the way for tailored interventions that preempt adaptive resistance, ultimately enhancing clinical outcomes in heterogeneous malignancies. Citation Format: Peter Oloche David. Decoding Tumor Complexity: An Integrative Framework Linking Chromatin Topology to Therapeutic Resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr B031.

BACKGROUND: Advances in CRISPR-based gene editing and stem cell-derived embryo models have created substantial biomedical opportunity alongside significant ethical and distributive concerns. Personalized genomic and regenerative therapies approved in the United States as of this writing carry list prices spanning approximately $2.2 million (Casgevy) to $4.25 million (Lenmeldy), with several other approved products priced between these figures (e.g., Lyfgenia, $3.1 million; Hemgenix, $3.5 million). This raises the question of how affordability, accessibility, ethical compliance, and distributional inequality can be jointly represented in a single, mathematically coherent index to support policy analysis. METHODS: We audited a previously proposed composite index, the Genomic Equity Accessibility Framework (GEAF = ((A x E) - (C x I))/(P + R) x 100), and identified dimensional incompatibility between its terms, an unbounded and not-independently-interpretable output range, and scenario inputs inconsistent with the cost estimates cited elsewhere in the same manuscript. We reconstructed GEAF as a normalized, bounded (0-100) weighted composite of six dimensionless components - accessibility, ethical compliance, affordability, equity (inverse inequality), policy strength, and research efficiency - derived using multi-attribute utility theory. We tested the reconstructed formula against algebraic edge cases, applied it to seven re-specified illustrative scenarios whose cost inputs are anchored to cited list-price literature, and conducted one-way, two-way, and Monte Carlo (10,000-iteration) probabilistic sensitivity analyses. RESULTS: The reconstructed index (GEAF') is bounded on [0,100], monotonic in every component, and behaves consistently at algebraic extremes. Across the seven illustrative scenarios, GEAF' ranged from 27.9 (no equity intervention, list-price cost) to 86.5 (fully optimized tiered-pricing and policy scenario). One-way sensitivity analysis identified cost (C) and the inequality factor (I) as the two largest single-parameter drivers of the index under baseline conditions, followed by ethical compliance (E). Monte Carlo simulation (10,000 iterations per regime) propagating stated, non-empirical parameter-uncertainty assumptions produced a baseline-scenario mean of 32.6 (90% interval, 5th-95th percentile, approximately 22.9-42.9) and an equity-intervention-scenario mean of 72.4 (90% interval approximately 62.9-81.1), with no overlap between the two simulated distributions. CONCLUSIONS: GEAF' provides a transparent, reproducible, and dimensionally coherent way to combine affordability, accessibility, ethical compliance, and distributional equity into a single comparative index for genomic and regenerative therapy policy scenarios. It is presented as a candidate conceptual and methodological framework, not as an empirically validated predictive or clinical tool; quantitative claims about real-world cost reductions have been removed unless directly supported by cited evidence. Prospective empirical calibration against real accessibility, pricing, and policy-outcome data is identified as a necessary next step before GEAF' is used in health technology assessment or resource-allocation decisions.

This repository contains the Python source code developed for the computational modeling, analysis, simulations, and visualizations associated with the research manuscript “Longitudinal ctDNA Constrains a Resistance-Evolution Model and Frames Counterfactual Adaptive Dosing in Neoadjuvantly Treated Breast Cancer.” The manuscript represents a substantially developed and revised stage of a research program previously disseminated under the title “Decoding Cancer Evolution: Computational Models for Tumor Dynamics and Precision Oncology” as an SSRN preprint (DOI: 10.2139/ssrn.6654018). An earlier abstract from this research program was peer reviewed and published as a conference abstract in Cancer Research, volume 85, Issue 23_Supplement (2025), under the title associated with AACR presentation B030 (DOI: 10.1158/1538-7445.CANEVOL25-B030). The present repository provides the computational code associated with the current manuscript and is deposited separately to support computational transparency, reproducibility, and independent examination of the methods and analyses.

This repository contains the Python source code developed for the computational modeling, analysis, simulations, and visualizations associated with the research manuscript “Longitudinal ctDNA Constrains a Resistance-Evolution Model and Frames Counterfactual Adaptive Dosing in Neoadjuvantly Treated Breast Cancer.” The manuscript represents a substantially developed and revised stage of a research program previously disseminated under the title “Decoding Cancer Evolution: Computational Models for Tumor Dynamics and Precision Oncology” as an SSRN preprint (DOI: 10.2139/ssrn.6654018). An earlier abstract from this research program was peer reviewed and published as a conference abstract in Cancer Research, volume 85, Issue 23_Supplement (2025), under the title associated with AACR presentation B030 (DOI: 10.1158/1538-7445.CANEVOL25-B030). The present repository provides the computational code associated with the current manuscript and is deposited separately to support computational transparency, reproducibility, and independent examination of the methods and analyses.

The fast growth of biotechnology, genetic engineering, and brain-computer interfaces has caused us to rethink what it means to be human, mixing biology and technology in new ways. This thesis looks into the philosophical basis of humanity and the heated discussion between transhumanism and supporters of "natural" humanity. It also considers the ethical issues regarding the rights of artificial intelligence (AI) and genetically modified humans. By drawing on insights from philosophy, bioethics, law, and technology studies, it argues that while these developments can improve human abilities, they bring serious moral concerns about fairness, identity, and how society includes everyone. The analysis suggests that giving equal rights to AI and enhanced humans is vital for a fair future. It proposes ways to achieve this, like human rights frameworks and inclusive governance. By critically examining both historical and modern sources, this work adds to ongoing discussions on posthumanism, highlighting the need for ethical protections against inequality. Ultimately, it proposes that the future of humanity lies in accepting hybridity while maintaining essential values of dignity and autonomy.

This research presents an interdisciplinary exploration of the ethical, economic, and policy challenges shaping the future of genomics and regenerative medicine. Focusing on transformative technologies such as CRISPR gene editing and synthetic embryo models, the study critically examines key bioethical dilemmas, including non-therapeutic genetic enhancement, the moral status of lab-generated embryos, and the global inconsistencies in stem cell governance.A central contribution of this work is the introduction of the Genomic Equity Accessibility Formula (GEAF), a novel mathematical framework designed to quantitatively evaluate and improve equitable access to personalized genomic therapies. By integrating variables such as treatment cost, accessibility, ethical compliance, inequality, policy strength, and research efficiency, GEAF provides a scalable tool for policymakers, researchers, and global health stakeholders to model and optimize equity-driven interventions.Through scenario-based simulations grounded in real-world economic estimates, the study demonstrates that targeted strategie, such as tiered pricing, public-private partnerships, blockchain-enabled data sharing, and AI-driven research optimization can reduce therapy costs by up to 70% for underserved populations while significantly improving access.The paper further proposes the Equitable Genomics Initiative (EGI), a global policy framework combining ethical governance mechanisms (e.g., Ethical Enhancement Councils, Tiered Moral Frameworks, and International Stem Cell Accords) with technological and financial innovations to ensure inclusive and responsible scientific progress.By bridging bioethics, health economics, and computational modeling, this work offers a forward-looking blueprint for aligning genomic innovation with global health equity, emphasizing that the benefits of regenerative medicine must be accessible to all, not just privileged populations.

This research presents an interdisciplinary exploration of the ethical, economic, and policy challenges shaping the future of genomics and regenerative medicine. Focusing on transformative technologies such as CRISPR gene editing and synthetic embryo models, the study critically examines key bioethical dilemmas, including non-therapeutic genetic enhancement, the moral status of lab-generated embryos, and the global inconsistencies in stem cell governance.A central contribution of this work is the introduction of the Genomic Equity Accessibility Formula (GEAF), a novel mathematical framework designed to quantitatively evaluate and improve equitable access to personalized genomic therapies. By integrating variables such as treatment cost, accessibility, ethical compliance, inequality, policy strength, and research efficiency, GEAF provides a scalable tool for policymakers, researchers, and global health stakeholders to model and optimize equity-driven interventions.Through scenario-based simulations grounded in real-world economic estimates, the study demonstrates that targeted strategie, such as tiered pricing, public-private partnerships, blockchain-enabled data sharing, and AI-driven research optimization can reduce therapy costs by up to 70% for underserved populations while significantly improving access.The paper further proposes the Equitable Genomics Initiative (EGI), a global policy framework combining ethical governance mechanisms (e.g., Ethical Enhancement Councils, Tiered Moral Frameworks, and International Stem Cell Accords) with technological and financial innovations to ensure inclusive and responsible scientific progress.By bridging bioethics, health economics, and computational modeling, this work offers a forward-looking blueprint for aligning genomic innovation with global health equity, emphasizing that the benefits of regenerative medicine must be accessible to all, not just privileged populations.

Abstract Cancer evolution, driven by genetic instability and clonal selection, challenges effective treatment. This review synthesizes computational approaches to model tumor progression, heterogeneity, and resistance, bridging biology with precision medicine. We integrate multi-omics data with mathematical frameworks to uncover adaptive behaviors, guiding therapies that exploit evolutionary vulnerabilities. Our methods span cancer’s multiscale nature. Ordinary differential equations (ODEs) model biochemical networks, like cyclin-dependent kinase dynamics, with cell cycle progression as \(\frac{d[CycD]}{dt} = k_s - k_d [CycD] - k_p [CycD][CDK4/6]\), revealing treatment effects. Probabilistic models, like branching processes, predict lineage evolution with exponential waiting times \(P(t) = \lambda e^{-\lambda t}\). Partial differential equations (PDEs) address tumor microenvironment influences, e.g., hypoxia-driven selection: \(\frac{\partial u}{\partial t} = D \nabla^2 u + f(u) - g(u,v)\), where \(u\) and \(v\) denote cell and nutrient density. Agent-based models (ABMs) capture phenotypic plasticity, while Boolean networks identify regulatory attractors. Dynamical systems theory quantifies chaos via Lyapunov exponents (\(\lambda = \lim_{t \to \infty} \frac{1}{t} \ln \left| \frac{\delta x(t)}{\delta x(0)} \right|\)) and fractal dimensions, signaling relapse risks. Algorithmic information dynamics, using Kolmogorov complexity \(K(s) = \min \{ |p| : U(p) = s \}\), infers causal gene networks via block decomposition. Machine learning, including recurrent neural networks, forecasts omics time-series, while phylogenetic tools like PiCnIc reconstruct clonal trajectories. Analysis of breast and pancreatic cancer datasets shows chaotic attractors linked to chemoresistance, with multifractal spectra (Hurst index \(H\)) quantifying growth irregularity. Adaptive dosing, modeled via optimal control, reduces resistance by 40-60%, with PDE-ABM hybrids predicting clonal expansion under therapies. In conclusion, these computational tools reveal cancer as an evolving ecosystem, enabling personalized, evolutionary-guided therapies to outmaneuver tumor adaptation for improved outcomes. Citation Format: Peter Oloche David. Decoding cancer evolution: Computational models for tumor dynamics and precision oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr B030.

Abstract In recent years, the field of therapeutic development has experienced a significant paradigm shift driven by strategic scientific innovations that prioritize safety and efficacy in therapeutic agents. This paper explores the intersection of advanced research methodologies, novel material sciences, and biotechnological advancements that are revolutionizing the approach to drug formulation and delivery systems. The integration of precision medicine principles alongside cutting-edge technologies allows for the design of therapeutics that are not only effective in targeting specific diseases but are also engineered to minimize adverse effects associated with conventional therapies. One focal point of this exploration is the application of nanotechnology in drug delivery systems. By utilizing nanoparticles, researchers can enhance the bioavailability of therapeutic agents while reducing systemic toxicity. This targeted approach enables the delivery of higher concentrations of drugs to the affected areas, thereby maximizing therapeutic efficacy and minimizing side effects. Additionally, the paper highlights the role of computational modeling and simulations in predicting the interaction of therapeutic agents at the molecular level, facilitating the identification of safer compounds through virtual screening methods. Another significant aspect addressed in this paper is the development of biologics and biosimilars as safer alternatives to traditional synthetic drugs. The advancement of biomanufacturing processes has made it feasible to produce complex molecules with high specificity and reduced immunogenicity, paving the way for innovative therapies that cater to individual patient profiles. Furthermore, the strategic incorporation of pharmacogenomics in drug development allows for the customization of therapeutic agents, ensuring that treatment regimens are tailored to the genetic makeup of patients, which can drastically improve safety and treatment outcomes. The paper also discusses the regulatory landscape surrounding the introduction of these innovative therapeutic agents. It emphasizes the need for adaptive regulatory frameworks that keep pace with scientific advancements while ensuring patient safety and efficacy. Collaborative efforts between researchers, regulatory bodies, and pharmaceutical companies are essential to facilitate the translation of innovative therapies from the laboratory to clinical practice. In conclusion, this paper underscores the critical importance of strategic scientific innovations in the quest for safer therapeutic agents. By harnessing the potential of advanced technologies, researchers can develop therapeutics that not only meet the growing demand for efficacy but also uphold the highest standards of safety, ultimately improving patient care and health outcomes. The findings presented advocate for continued investment in research and collaboration across disciplines to drive future advancements in therapeutic development. Citation Format: Peter O. David. Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A001

Abstract Cancer Predisposition is a complex and multifaceted topic that continues to intrigue researchers and scientists for decades. This thesis delves into the intricate web of factors that contribute to an individual's predisposition to cancer, aiming to unravel the mysteries surrounding this phenomenon. Through a comprehensive review of existing literature and cutting-edge research findings, the paper explores the genetic, environmental, and lifestyle factors that play a crucial role in predisposing individuals to cancer. By examining the interplay between genetic mutations, familial history, and environmental exposures, this study sheds light on the intricate mechanisms underlying cancer predisposition. Furthermore, the thesis investigates the role of epigenetic modifications, immune system dysregulation, and metabolic abnormalities in shaping an individual's susceptibility to cancer. The intricate interactions between these factors and their collective impact on cancer risk are analyzed, providing valuable insights into the underlying causes of cancer predisposition. By synthesizing the latest research findings in the field, this thesis offers a comprehensive overview of the complex nature of cancer predisposition and its multifactorial etiology. Ultimately, this study aims to contribute to our understanding of the intricate interplay between genetics, environment, and lifestyle in shaping an individual's susceptibility to cancer, paving the way for targeted prevention and personalized treatment strategies in the fight against this devastating disease. Citation Format: Peter Oloche O. David. Unraveling the complex interplay of genetic, environmental, and lifestyle factors in cancer predisposition: A comprehensive review and analysis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A028.

Abstract Synthetic lethality discovery is an emerging field of research that aims to reshape the landscape of cancer treatment, offering new hope and possibilities for patients faced with challenging diagnoses. In this article, we explored the cutting-edge technologies shaping the future of synthetic lathality research. Artificial intelligence and machine learning are not just buzzwords but indispensable tools that are shaping the field of precision medicine and personalized cancer treatment. By leveraging the power of computational tools, scientists can accelerate the identification of synthetic lethal interactions and pave the way for the development of more effective targeted cancer therapies. Through case studies, researchers can uncover the functional genomics of complex diseases and identify novel drug targets for cancer therapy. The integration of multi-omics approaches not only enhances the depth of analysis but also offers a more nuanced understanding of the molecular mechanisms driving synthetic lethal interactions, paving the way to unlock novel therapeutics and personalized treatment strategies. In addition, the integration of high-throughput screening techniques, such as CRISPR-Cas9 gene editing, opens up new avenues for identifying new drug targets and therapeutic strategies, ultimately paving a new era where personalized and targeted therapies redefine the way we approach cancer care, ushering in a novel era of precision and efficacy in medicine. Citation Format: Peter Oloche David. Unveiling the future: Exploring cutting-edge technologies for synthetic lethality discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B014.