PRESENTERS

Department of Computer Science & Engineering

Using Machine Learning on Autonomous Drones for route optimization

More info to come.

RESEARCHERS: Riddhi B., Amador Valley High School '27, Claire L., Basis Independent Silicon Valley '27, Kaustubh L., Amador Valley High School '25, Aarsh M., The Quarry Lane School '25, Nikhil S., American High School '26, Naren V., Heritage High '25, Shaunak J., American High School '26, Tanish P., American High School '26, Taran A., Dougherty Valley High '28, Zheng X., Irvington High School '28, Kush G., Lynbrook High School '26

ADVISOR: Subramaniam, Data Science

KEYWORDS: More info to come.

Department of Computer Science & Engineering

A Comparison of Supervised Learning and Deep Reinforcement Learning for Autonomous Driving

More info to come.

RESEARCHERS: Divya P., Saint Francis High School '25, Mihir R., Archbishop Mitty High School '26, Katrina R., Monta Vista High School '26, Truman Y., Basis Independent Silicon Valley '25, Ishan G., Fremont High School '25, Ansh N., Lynbrook High School '28, Dhanush A., American High School '26

ADVISOR: Subramaniam, Data Science

KEYWORDS: More info to come.

Department of Computer Science & Engineering

Using machine learning to diagnose spinal defects, injuries and illnesses

More info to come.

RESEARCHERS: Andrew Duval, Leland High '26; Navya Rawal, Dougherty Valley High School '25

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: More info to come.

Department of Chemistry, Biochemistry & Physics 

Genome Variation Analysis between Different American Ethnic Groups to Improve Precision Medicine

Precision medicine, an emerging medical research field, focuses on human genetic variations in populations and individuals and how they influence the disease treatment process. The field aims to create personalized medicine for people with specific genotypes, therefore improving treatment efficiency (Kraink & Fuentes, 2022). There is a need for greater ethnic and genetic diversity in studies surrounding this field. This project studied data from five different populations in the United States: Mexican Ancestry in Los Angeles, California; Utah residents (CEPH) with Northern and Western European ancestry, African Ancestry in Southwest US; Gujarati Indians in Houston, TX; and Hawaiian in USA (SGDP) from the International Genome Sample Resource. All of these populations were from phase 3 of the 1000 Genomes Project, except for the Hawaiian sample, which was from the Simons Genome Diversity Project. (Fairley et al., 2019) We have used the bioinformatics platform Galaxy and tools downloaded on the ASDRP computing cluster to run variant calling with the sequence reads of these populations against the novel T2T-CHM13 reference genome. We aim to discover novel variants from the sample data in our five populations to determine whether personalized drug treatment is needed among these different populations. 

RESEARCHERS: Kanika Rawat, Notre Dame High School San Jose '26

ADVISOR: Cunha, Bioinformatics and Cancer Biology

KEYWORDS: Bioinformatics | Precision Medicine | Genetics | Variant Calling 

Department of Chemistry, Biochemistry & Physics 

Scalable formal synthesis of (+)-etomoxir without pyrophoric reagents, enabled by a one-flask tandem aldol-Luche sequence

More info to come.

RESEARCHERS: Flora Xie, California High School '25

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS: More info to come.

Department of Computer Science & Engineering

Using Autonomous Drone for Environmental mapping and forrest health assessments.

More info to come.

RESEARCHERS: Andrew Duval, Leland High '26; Navya Rawal, Dougherty Valley High School '25

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: More info to come.

Department of Chemistry, Biochemistry & Physics 

Tabor Factor Determination as a Function of Strengthening Mechanism in Cu-Based Alloys

Determining Tabor factor in correspondence to microstructure and composition may lead the way to inexpensive, non-destructive methodology to predict tensile properties of bulk materials from localized hardness measurements which is important for developing preventive maintenance procedures or scaling up R&D operations. Tabor factor of commercial copper C110 are planned to be investigated as a function of heat treatment ( grain size) to lay a foundation of a study on if and how Tabor factor depends on mechanisms of strengthening metals and alloys.

RESEARCHERS: Rui Z., Irvington High School '25, Anton K., Bellarmine College Preparatory '26, Erin W., Henry M Gunn High School '26, Saketh P., Dougherty Valley High School '26

ADVISOR: Starostina, Materials Science

KEYWORDS: Material Sciences | Copper | Tabor Factor | Vickers Hardness | Copper Microstructure

Department of Chemistry, Biochemistry & Physics 

Evaluation of Mechanical Properties of 3D-printed PLA 

Additive manufacturing is an emerging manufacturing technique that can produce products with complex geometry infeasible by traditional subtractive methods. Fused Deposition Modeling (FDM) is one of the most popular methods of additive manufacturing and involves extruding a filament, typically a polymer, onto a hot plate. Polylactic acid (PLA) is a common polymer used by FDM printers due to its low melting point, minimal warpage, and relatively low cost.  It is generally understood that additive manufacturing processes yield inferior strength parts compared to subtractive methods. Ultimate tensile strength (UTS) is the measure of the maximum stress of a material before failure. Vickers Hardness test measures the ability of material to withstand force without deformation. In accordance with the ASTM standard, printed and traditional specimens are tested with the strain and stress data collected. UTS and Hardness testings allow for better understanding of the mechanical properties of 3D printing material. This will allow for better comparison with traditionally manufactured products.

RESEARCHERS: Divya E., American High School '26, Hanming Z., Independence High School '25, Caden W., Junipero Serra High School '25

ADVISOR: Starostina, Materials Science

KEYWORDS: Material Science| Mechanical Engineering | Physics | 3D printing 

Department of Computer Science & Engineering

A Novel Method for the Characterization of Conformational Isomers using Stochastic Monte Carlo Analysis of Quantum Properties

Determining the molecular Hamiltonian proves to be a useful tool in understanding a molecule’s properties, including energy changes in reactions as well as molecular stability. The Hartree-Fock method has been a long-standing approach for approximating the Schrödinger equation to help find molecular energies. Post-Hartree-Fock methods have been created to adjust for smaller perturbation energies, including the Møller–Plesset perturbation theory among others.This work aims to describe the relationship between the Hamiltonians of several computer generated isomers, and in particular, a novel method for the analysis of conformational isomers in hydrocarbons chains that are alkanes. Given a single molecular structure, the methodology provided in this work aims to find possible conformers of the molecule. This is done through the analysis of quantum properties using Monte Carlo methodology and classical geometric clustering methods. Molecular geometries were evaluated using the Computational Chemistry Comparison and Benchmark Database (CCCBDB) with the MP2/cc-pVDZ basis set.

RESEARCHERS: Rishab G., Albany High School '26, Shashank K., Irvington High School '25, Jayden L., Monta Vista High School '25

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Hartree-Fock | Post-Hartree-Fock | Møller-Plesset Perturbation Theory | Schrodinger’s equation | Conformational Isomers | Monte Carlo

Department of Computer Science & Engineering

Hybrid Quantum-Classical Graph Generative Models To Target Alzheimer's Disease

Contemporary drug discovery and development processes require billions of dollars and lengthy amounts of time, which is why researchers are utilizing computational chemistry methods like machine learning to speed up molecular synthesis pathways. Alzheimer's disease presents a specific challenge, as treatment requires molecules that can penetrate the blood-brain barrier (BBB). There are currently no effective cures for Alzheimer's disease. Our group is currently working on multiple models utilizing various machine learning techniques to generate chemically stable, novel, and druglike molecules that are of the right size and fit the molecular descriptors to penetrate the BBB. In previous work, our group developed QNetGAN v2, a model that utilizes quantum computing and generative adversarial networks (GANs) to generate chemically feasible molecules for general drug discovery with an 89.23% success rate, generating 116 out of 130 structurally valid molecules. In addition to building off the GAN structure used in our original model to target BBB permeability, our group is working on several other novel models, including message passing neural networks (QMPNN) and several variational auto-encoder (VAE) based methods.

RESEARCHERS: Linda Chang, Homestead High School '25, Nitya Pisolkar, Archbishop Mitty High School '27, Sahil Vijay, Leigh High School '26, Narasimhan Prasana, Mission San Jose High School '25

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Machine Learning | Quantum Computing | Drug Discovery | Computational Chemistry | Alzheimer's Disease

Department of Computer Science & Engineering

Theoretical Study of Quantum Dots in Noise-Resistant Quantum Key Distribution Systems

Quantum key distribution (QKD) systems hold the potential for secure communication by leveraging the laws of quantum mechanics. However, challenges arising from noise and environmental interactions can compromise the security and efficiency of QKD systems. This research aims to explore the theoretical aspects of utilizing quantum dots within noise-resistant QKD systems. By leveraging advanced simulation techniques on a high-performance computing cluster, we investigate the impact of quantum dots on enhancing the security and reliability of QKD systems.

RESEARCHERS: Prahlad Saravanapriyan, Washington High School '25, Praneel Samall, Academy of the Canyons '25, Aditya Das, American High School '26, Samuel L., Saint Francis High School '26, Ezana Makonnen, Archbishop Mitty High School '26, Sophia Ren, The Madeira School '26, Dhanya Ganesh, Basis Independent Fremont '27, Elaine Huang, The Harker School '28

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Quantum Dots | Quantum Key Distribution

Department of Computer Science & Engineering

Diagnosing different types of skin cancer using Machine Learning (CNNs)

Skin cancer poses a significant health challenge, with early diagnosis crucial for effective treatment. Inspection by dermatologists remains the standard for diagnosis, but it can be subjective and prone to error. This project explores the potential of convolutional neural networks (CNNs) as a powerful tool for skin lesion classification, aiming to improve accuracy and accessibility of diagnosis and ultimately contribute to better patient outcomes. Leveraging the data of over 10,000 images of pigmented skin lesions categorized into seven diagnostic classes (including benign and malignant tumors), we will use a deep learning model based on CNNs and Text based models. This project showcases the power of deep learning and multimodal models in revolutionizing skin cancer diagnosis.

RESEARCHERS: Anav Bordia, Basis Independent Silicon Valley '25, Ayaan Chawla, San Ramon Valley High School '25

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Machine Learning | Skin Cancer | Multimodal

Department of Chemistry, Biochemistry & Physics  

Discovery, Synthesis, and Optimization of 5-phenylisoxazole Based Covalent Inhibitors Targeting G12C Mutant KRAS for the Treatment of Cancer and Optimized Preparation of Nucleozin and analogs for Influenza inhibition: A unified strategy for challenging amide coupling reactions in medicinal chemistry

RESEARCHERS: Natalie Brahan, Irvington High School ‘25

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS:  More info to come

Department of Chemistry, Biochemistry & Physics 

Evaluation of Dihedral Angle of Twin Boundaries in Copper

Knowledge on interfacial free energies, or ratio of energies, of metals alloys is one the most sought after parameters in computational materials science and practical metallurgical applications. We propose the usage of an atomic force microscope (AFM) as a tool to evaluate the ratio of the twin boundaries to the surface free energy in copper. 3D printed models of twin boundaries were constructed on an atomic level scale. Heat treatment of "as received" copper samples was performed at 900º C and 800ºC for 1 hour to grow the copper's grains until it was suitable for observations. Metallurgically polished and etched samples were prepared in the ASDRP lab for optical, electron microscopy and AFM evaluations. We will discuss our results and future plans during the presentation.

RESEARCHERS: Phinna Yin, Washington High School '26, Saahithi Srikanth, Monta Vista High School '27, Darvas Gao, Washington High School '25, Meghana Satish, Mission San Jose High School '27, Joseph Miao, Mission San Jose High School '27

ADVISOR: Starostina, Materials Science

KEYWORDS: Ultrasound, Ultrasonography, Deep Learning, Convolutional Neural Network, CNN, Breast Cancer

Department of Chemistry, Biochemistry & Physics 

Machine Learning in SEM Imaging of nanoparticles

"Elements of artificial intelligence and machine learning (ML) will become increasingly prevalent in the established field of electron microscopy. The myriad tasks of scanning electron microscope(SEM) have many routines that are time consuming yet require significant degrees of precision. We intend to focus on using machine learning to recognize nanoparticles in SEM images. We can use the parameters outlined for the command line to invoke the varied command-&-control (CnC) applications on the SEM, setting initial conditions, then use Remote Procedure Calls (RPCs) or provided Application Programming Interfaces (APIs) to implement for concurrent feedback loops. The implementation of ML will be discussed."

RESEARCHERS: Ezra Spivak, Amogh Khandkar, Ojas Rokade, Neev Tamboli, Eshan Gupta, Ilina Gupta, Sophia Hale

ADVISOR: Starostina, Materials Science

KEYWORDS: Scanning Electron Microscopy  | Nanoparticles  | Machine learning  | Application Programming Interfaces

Department of Computer Science & Engineering

Analyzing Automated Breast Ultrasound Images using Deep Learning and Convolutional Neural Networks to Detect Breast Cancer

Using Deep Learning and CNNs, we will create a model that detects breast cancer by analyzing 3D ultrasound images. We will compare multiple CNN architectures, including a ResNet-18 and a VGG-16 architecture. All will be for feature extraction. Our model will also include a Max Pooling Layer which shrinks the feature vector while keeping the most informative features. The remaining features will be concatenated and will be put through an output layer which will determine whether there is breast cancer present in the image.

RESEARCHERS: Andrew Lin, Basis Independent Silicon Valley ‘27; Gautam Taneja, Cupertino High school ‘26; Bhavya Dwivedi, Emerald High School ‘27

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Ultrasound, Ultrasonography, Deep Learning, Convolutional Neural Network, CNN, Breast Cancer

Department of Computer Science & Engineering

Predicting the Chaotic Motion of the Multi-Pendulum System Using Machine Learning and Neural Networks

The multi-pendulum system is a classic example of a chaotic mechanical system, characterized by its sensitive dependence on initial conditions and complex dynamic behavior. Predicting the trajectory of such a system poses a significant challenge due to its non-linear nature and the interdependence of its components. In this study, we explore the application of machine learning techniques and neural networks to predict the motion of a double pendulum. We begin by generating synthetic data representing the angles of the double pendulum over time using an implicit iterative differential equation solving technique called Runge Kutta Method for 4th Order Differential equations. After normalizing this data for effective model training, a comparative analysis involving 16 different machine learning models and neural networks was conducted with historical angle data used as features to ultimately forecast future states of the pendulum. The performance of the models are evaluated using metrics such as Root Mean Squared Error (RMSE) and the R-squared (R2) score. Subsequently, the predicted angles are transformed into Cartesian coordinates to visualize the trajectory of the pendulum.

RESEARCHERS: Hruday Nara, Leigh High School ‘26, Vasista Ramachandruni, Milpitas High School ‘25, Geo Lalu, Milpitas Middle College High School ‘25, Sabrina Yang, Presentation High School ‘25

ADVISOR: Akl Lab, Machine Learning for Condensed Matter Physics

KEYWORDS: Physics | Machine Learning | Chaotic Systems | Chaos Theory | Neural Networks | Double Pendulum | Triple Pendulum | Computational Physics | R-Squared | Root Mean Squared Error | 4th Order Runge-Kutta Method | 4th Order Differential Equations

Department of Computer Science & Engineering

Quantum Computing Error Mitigation

Quantum computers require quantum bits (qubits) to undergo many operations during large-scale computing projects. During these projects, qubits can be affected by internal or external noise, which is the source of quantum error. This error can cause quantum computers to provide incorrect information, making their outputs unreliable. In order to generate accurate results, quantum error mitigation catches and corrects errors before they affect the outcomes of quantum computers. Surface codes in particular help reorganize qubits into a 2D lattice shape, where they can be measured indirectly to find their original state and the start and end points of any error propagation. Minimum Weight Perfect Matching (MWPM) is a popular method to calculate these propagation paths based on the data that the surface code returns, but recent studies have shown that using machine learning models to detect these surface code errors returns a higher accuracy. Our project utilizes Graphic Neural Networks (GNNs), a certain type of neural network that performs on graphical structures such as the 2D surface code lattice. These GNNs will be trained to predict the path of error propagation through the surface code so that correction methods can most efficiently revert the qubits back to their original state.

RESEARCHERS: Neel Chellapilla, The College Preparatory School  ‘27

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: Quantum Computing | Machine Learning | Artificial Intelligence | GNNs

Department of Chemistry, Biochemistry & Physics  

Strategies towards developing reagents for quantitative antibody labeling

More info to come.

RESEARCHERS: Hengrui Chen, The King's Academy ‘25

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS:  

Department of Computer Science & Engineering

Using drones for inspection of power lines to identify fire hazards.

More info to come.

RESEARCHERS: Tuhin Mythil, Bellarmine College Preparatory '27, Yuvit Monani., Irvington High School '27, Vihan Kalsi, Basis Independent Silicon Valley '26

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: 

Department of Computer Science & Engineering

Using Quantum Neural Networks (QNNs), Quantum Vision Transformers (QVT), and the Mathematical Morphological Reconstruction Algorithm (MMR) for Brain Tumor Detection

Brain tumors affect millions around the world, so detection is critical to helping doctors determine treatment. Currently, radiologists manually identify tumors through MRI (Magnetic Resonance Imaging) scans; however, this poses several limitations: it creates a heavy reliance on the experience of radiologists, has become increasingly costly and time-consuming, and is not as accessible to areas that lack the necessary resources and doctors. With the advancement of deep learning algorithms, a more accessible and efficient solution is possible. Given the existing research in classical Convolutional Neural Networks (CNNs) for tumor detection, Quantum Convolutional Neural Networks (QCNNs) and Quantum Vision Transformers (QVT) offer a promising approach to the problem. Mathematical Morphological Reconstruction (MMR), another image processing method, provides a relative metric for success in the QCNN, and is another classical alternative to CNNs. This research compares the accuracy and computational speed of the MMR, QCNN, QVT, and CNN algorithms to determine whether introducing a quantum aspect presents any noticeable advantage. To build these models, extensive datasets of MRI brain scans were collected. The MMR algorithm involved applying various techniques such as dilation, erosion, and skull stripping through OpenCV2's morphology functions. The QCNN algorithm utilizes quantum power to encode the data into a parametrized quantum circuit and apply convolutional and pooling layers. In terms of future steps, QVTs will be implemented with QCNNs for higher spatial understanding. So far, our results indicate that the MMR algorithm achieved up to 92% accuracy. These results will be compared with the accuracy of the QCNN, QVT, and CNN algorithms.

RESEARCHERS: Tiffany Liu, Quarry Lane ‘25, Riddhi Sharma, Evergreen Valley ‘26

ADVISOR: McMahan, Quantum Computing & Computer Science 

KEYWORDS: More info to come

Department of Chemistry, Biochemistry & Physics  

Design and Synthesis of Novel Carmofur Analogs for In Vitro Evaluation Against Cancer Cells and the SARS-CoV-2 Main Protease

During COVID-19, carmofur, a 5-fluorouracil derivative initially developed as an antineoplastic agent against colorectal cancer, was identified through a high-throughput drug repurposing screen as a potent covalent inhibitor of the SARS-CoV-2 main protease (Mpro). Thus, it is a promising therapeutic candidate against COVID-19. Utilizing 19F NMR to quantitatively track reaction rates in various reaction conditions, our group optimized the synthesis of carmofur and synthesized eleven novel carmofur analogs aimed at exploring the impact of structural modification on biological activity. To evaluate the efficacy of our compounds in vitro, we performed MTT assays on various cancer cell lines to probe their anti-proliferative activity and a colorimetric assay to assess our analogs’ inhibition of Mpro. We found that certain analogs ourperformed carmofur in their antiproliferative activity and inhibition of Mpro.

RESEARCHERS: Eileen Zhang, Amador Valley High School ‘25

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS:  Quantum Computing | Brain Tumors | Machine Learning| Mathematical Morphological Reconstruction | Convolutional Neural Networks

Department of Chemistry, Biochemistry & Physics  

Probing the SAR of novel C-4 carbonate and carbamate podophyllotoxin analogs and photocages, tubulin inhibitors with antiproliferative activity in breast and colon cancer cell models

Podophyllotoxin, a natural product isolated from the Podophyllum family used by various indigenous groups for medicinal purposes, still holds great importance in anticancer presence today through its unique ability to inhibit tubulin polymerization. Previously, we and others reported in the literature that the C-4 hydroxyl of podophyllotoxin has demonstrated the capacity to modulate its biological activity. While already previously reporting analogs of podophyllotoxin bearing aliphatic esters in varying bulk, a clear trend has been established of lower in vitro potency with increasing aliphatic bulk through cell viability assays and cell cycle analysis, however, this trend was not consistent with cell-free experiments or with computer models, suggesting an interplay of both steric bulk and logP in driving activity of these compounds. With these initial, preliminary conclusions drawn, we have expanded our library of compounds including a library of carbonates and carbamates to more clearly establish an SAR involving twenty five novel carbonate and carbamate analogs at the C-4 position. Highlighted in this talk will be a proposed PK model for the complementary roles of target lipophilicity and steric demand at this position,  as well as the first synthesis of C4-β-carbamates on the podophyllotoxin scaffold. These twenty-five compounds were subsequently evaluated for potency against a broad array of cancer cell lines, including HT-29 and HCT-116 colon cancer cells, and T47D, MCF7, MDA-MB-231, and MDA-MB-468 breast cancer cells through high-throughput antiproliferative assays, flow cytometry analysis of cell state, and cell-free tubulin inhibition experiments. Our initial results suggest that the SAR at C-4 is more complex than either steric demand or logP of our analogs, and that this pattern gives rise to unique cancer cell line selectivites not previously described. 

In addition, identifying that podophyllotoxin is highly toxic through in vitro assays, killing cells at the nanomolar concentrations, we have taken an approach to increase control and specificity of the drug. With the use of prodrugs, a targeted approach towards drug release, as well as an overall reduction in the drugs toxicity but not potency is implemented into the realm of localized therapeutics. With preliminary results and a scalable synthetic preparation of C-4 carbonates and carbamates in hand, we turned our attention to installation of photolabile 2-nitrobenzyl carbonates on podophyllotoxin. Consistent with prior expectations, the addition of electron-donating groups such as methoxy ethers on the 2-nitrobenzyl arene ring results in changes in optimal release wavelength, and differential kinetics of photorelease, as determined by high performance liquid chromatography (in collaboration with the Yamamoto lab) and liquid chromatography - mass spectrometry.

RESEARCHERS: Hasini Menta, California High ‘25

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS:  Podophyllotoxin | Tubulin Inhibitors | Antiproliferative Activity | Breast Cancer | Colon Cancer | SAR | Carbonates | Carbamates | Photocages | Prodrugs

Department of Chemistry, Biochemistry & Physics  

Evaluation of Bio-inspired Ionizable Lipids for Lipid Nanoparticle mRNA Delivery, and Synthesis and bioimaging applications of novel bifunctional membrane-nucleic acid fluorescent lipids

Lipid nanoparticles (LNPs) have demonstrated exceptional promise as one of the most clinically advanced non-viral vehicles for messenger RNA (mRNA) delivery in vivo, as seen in recent developments of FDA-approved mRNA vaccines against SARS-CoV-2 infections. Typically composed of four main components–PEGylated lipids, phospholipids, cholesterol, and ionizable/cationic lipids, LNPs depend on branched ionizable lipids to play a major role in protecting the mRNA and improving cytosolic delivery. When in acidic pH, ionizable lipids are positively charged and spontaneously complex with polyanionic nucleic acids in the formation of LNPs; at physiological pH these ionizable lipids become charge-neutral, facilitating endosomal escape upon cell intake and protonation. However, most current clinically-available ionizable lipids require multi-step synthetic routes to manufacture. As a potential solution, we focused our efforts on designing a short, highly scalable, and efficient synthesis capable of producing biofriendly, non-toxic ionizable lipids inspired by nature. Here, we report a short synthesis producing a library of twenty-two lipids bearing unique phytochemical-derived acids containing linear and branched lipid tails, following an ionizable tertiary amine head group. Preliminary studies suggest that our ionizable lipids efficiently encapsulate FITC-labeled mRNA into nanoparticles and may be viable candidates for nucleic acid delivery. Further, we rationalize these results with in-silico modeling of the biophysical properties of these lipids.

In the second part of this talk, we wish to disclose a novel class of fluorescent lipids based on amonafide, a DNA-binding inhibitor of topoisomerase II. Cellular compartmentalization is vital in providing a variety of unique environments within a single cell. While the importance of these compartments and their facilitated functions are well acknowledged, the boundaries between them are poorly understood. Several studies report exciting discoveries in the realm of nucleic acid-lipid membrane systems, which enable processes like proper cytoskeletal and post-mitosis nuclear organization, and regulate cellular development and differentiation. Several heterocyclic lipidic dyes have been used to investigate phospholipid membranes. Thus, dimeric fluorophores containing an amonafide—a heterocyclic DNA-intercalator and topoisomerase II inhibitor—core and lipid tails may be promising imaging agents. Given its biological mechanism, amonafide is typically internalized within cell nuclei. However, upon addition of a hydrocarbon lipid tail, preliminary studies suggest that the fluorophore conjugate begins to localize along extranuclear membranes. Although the specific ramifications of this differentiated localization are unknown, these dimeric fluorophores have the potential to interact with nucleic acids outside of the nucleus, and our results suggest that they may be promising intracellular imaging agents.

RESEARCHERS: Shreya Somani, Lynbrook High ‘26 

ADVISOR: Njoo Lab Synthesis | Physical Organic Chemistry | Catalysis | Chemical Biology | Spectroscopy | Medicinal Chemistry 

KEYWORDS:  Ionizable Lipids | Lipid Nanoparticle | mRNA Delivery | Synthesis | Bioimaging | Fluorescent Lipids | mRNA Vaccines | SARS-CoV-2 | PEGylated Lipids | Phospholipids | Cholesterol | Cationic Lipids | Cytosolic Delivery | Polyanionic Nucleic Acids | Endosomal Escape | Biofriendly | Phytochemical-derived Acids | Ionizable Tertiary Amine | Encapsulation | FITC-labeled mRNA | Nanoparticles