Where I want the research to go.
My longer-term aim is to understand and improve the relationship between therapeutic design, exposure at a disease-relevant site and cellular response. I would develop that question within one laboratory's strengths, whether the decisive experiment concerns a ligand, a delivery barrier, a signalling pathway or a tissue model. The approaches below are different possible investigations, not consecutive requirements for a single doctorate.
Experiment-led research beyond AI. I am interested in rigorous laboratory questions in synthetic and medicinal chemistry, chemical biology, experimental pharmacology, pharmaceutics, biomaterials and cell-based disease models. My computational work is one part of that record, not a prerequisite for a future project. A carefully controlled synthesis, a pharmacological mechanism or an experimentally useful material can be the whole research question.
Antibody–drug conjugates (ADCs) and targeted bioconjugates. I want to study how antigen selection and internalisation interact with conjugation chemistry, linker stability and cleavage, payload properties, intracellular trafficking, pharmacokinetics and resistance in heterogeneous tumours. My published ADC analysis ↗ is part of my scientific writing; experimental ADC design and evaluation remain a prospective research interest, not a claim of completed bench work.
PROSPECTIVE RESEARCH / INTEGRATED DRUG DISCOVERY
From a cellular target to a molecule that can be made and tested.
I am interested in research programmes that connect target identification to chemical and biological production rather than treating computational discovery and synthesis as unrelated exercises. A possible starting point would be to define a disease-relevant cell state, prioritize candidate proteins or pathways using perturbation and expression data, then test whether changing that target alters the phenotype. This causal step must precede a confident claim of target validation.
For a selected target, I would compare known ligands, endogenous metabolites, natural-product scaffolds and chemically accessible analogues. Structure-based virtual screening, docking and molecular dynamics could narrow the field, while measured binding, target engagement, selectivity and phenotypic assays would determine which predictions survive experimental testing. Promising chemical structures could then be traced backwards through synthetic intermediates or biological precursors, with enzyme databases, metabolic-network reconstruction and strain or cell-host selection used to evaluate biosynthetic and chemoenzymatic opportunities.
The manufacturing question interests me as much as the molecular one: computer-assisted retrosynthesis, reaction-condition prediction, batch and continuous-flow synthesis, immobilized biocatalysts and in-line analytical monitoring could provide different routes to the same candidate. Automated high-throughput screening would test reproducible concentration–response, orthogonal mechanisms and counter-screens; design of experiments and active-learning or Bayesian optimization could then balance potency, selectivity, yield, stability, waste and throughput. The exact workflow would depend on the target and the capabilities of the host laboratory, not on a requirement to use every technology.
Methodological context: Ha et al., Cell Chemical Biology (2021): experimental target identification ↗ · Kataria et al., RSC Medicinal Chemistry (2024): computational target and lead discovery ↗ · Longer research question ↗.
Explore four experimental approaches and renal-therapeutics examples
Worked research questions, relevant controls, proposed readouts and referenced studies. These are future directions, not completed experiments or a fixed PhD project.
01 / DISCOVERY
Find the state that matters.
Before choosing a drug, determine which molecular interaction or cell state actually drives the phenotype. Protein location, conformation and timing can matter as much as target abundance. Chemical probes, genetic perturbation and functional rescue would test that dependence.
Decisive experiment: does altering the target change the disease-relevant response, rather than binding to it alone?
Example in chemical biology ↗
02 / DELIVERY & MATERIALS
Discover where function is lost.
For a drug or RNA cargo, I would distinguish formulation stability, administered dose, tissue access, cellular uptake and productive intracellular activity. The same logic applies to a surface-modified biomaterial, where chemical treatment must be linked to a measurable function.
Decisive experiment: which step limits effective, tolerable exposure when tested against a matched control?
My current formulation questions ↗
03 / HUMAN BIOLOGY
Measure the transition, not only the endpoint.
Longitudinal cell models, organoids and microphysiological systems could help distinguish early injury, repair and progression. In these systems, controlled forces, transport and cell–cell interactions are hypotheses to examine, not guarantees of clinical relevance.
Decisive experiment: does the model resolve a causal biological transition that a simpler experiment misses?
Pulmonary experimental foundation ↗
04 / PREDICTIVE SCIENCE
Make a model risk being wrong.
Computational and mechanistic models can connect molecular properties, material processing, delivery and response. Their value becomes clearer when they specify a prospective prediction, its uncertainty and a simpler baseline before new measurements arrive.
Decisive experiment: can the model predict an unseen result and identify what should be measured next?
Computational research ↗
ADDITIONAL RESEARCH INTEREST / RENAL THERAPEUTICS & DELIVERY
The kidney is not one therapeutic destination.
A glomerular podocyte, an injured proximal-tubule cell and the epithelium lining a renal cyst are different targets, with different access routes and pharmacology. I want to investigate kidney disease therapeutics and the molecular engineering needed to deliver treatment to the relevant renal compartment. The central challenge is to distinguish accumulation in a kidney from entry into the intended cells and, finally, from functional delivery that changes the intended biological response. Glomerular filtration, luminal reabsorption, basolateral access, transporters and disease-related changes in tissue permeability must shape the design, not be treated as afterthoughts.
01 / AKI AND CKD
Can injured tubules be treated without exposing the whole kidney?
In acute kidney injury and progression towards chronic fibrosis, I am interested in injury-conditioned targeting of tubular epithelium. KIM-1/HAVCR1 can serve as an uptake-associated marker in damaged tubules; megalin/cubilin and tubular transporters create different access routes whose activity may change during injury. A 2025 study showed KIM-1-associated delivery of biomimetic HDL nanoparticles carrying triptolide and nintedanib in mouse models of fibrosis. The result supports a targeting hypothesis, not proven human efficacy.
First test: compare free drug, carrier alone, untargeted cargo and targeted cargo, then measure cell-resolved uptake, pharmacodynamic activity, injury and off-organ toxicity. Demonstrate the mechanism of uptake separately from a reduction in fibrosis markers.
He et al., Nature Communications (2025) ↗
02 / GENETIC RENAL DISEASE
Can a therapy reach the cells lining a kidney cyst?
Autosomal dominant polycystic kidney disease, especially PKD1/PKD2-associated biology, offers a clear distinction between molecular correction and control of cyst-promoting pathways. A July 2026 preprint, not yet peer-reviewed, reported peptide-functionalized micelles delivering siRNAs against TMEM16A and MCP-1 to collecting duct epithelial cells. Separately, a peer-reviewed mouse study demonstrated AAV-mediated base editing of a disease-associated PKD1 variant. Neither is an approved renal gene therapy.
First test: validate collecting-duct specificity and productive knockdown or restored protein function in matched healthy and cystic renal models; compare cyst growth, inflammatory signals, cargo integrity and systemic distribution.
Giblin et al., bioRxiv preprint (July 2026) ↗ · ADPKD base-editing study, Nature Communications (2025) ↗
03 / GLOMERULAR AND IMMUNE DISEASE
When is renal delivery the right intervention?
IgA nephropathy, complement-mediated glomerular disease and podocyte injury connect immunopharmacology with tissue-selective exposure. A target may reside in the circulating immune system rather than in renal parenchyma: APRIL-directed antibodies and complement factor-B inhibition illustrate why a renal nanocarrier is not automatically preferable. By contrast, a locally acting podocyte or glomerular therapy requires testing its access through the glomerular endothelium and basement membrane.
First test: establish where the causal target acts, then compare systemic pathway modulation with a locally directed intervention using target engagement, proteinuria-relevant functional endpoints and renal safety.
FDA: APRIL-directed therapy in IgA nephropathy (2025) ↗ · Huang et al., renal nanomedicine physiology (2024) ↗
04 / HUMAN RENAL MODELS
Which exposure measurements predict renal function?
Human proximal-tubule-on-chip systems derived from kidney organoids can model polarized transport, including OAT1/3 and OCT2, more faithfully than some conventional immortalized-cell systems. For a renal carrier, I would pair physicochemical characterization with transporter function, directional epithelial transport, intracellular cargo activity and nephrotoxicity. Computational clearance or PBPK models would earn their place through prospective agreement with experimental measurements.
First test: benchmark a transporter-competent chip against a simpler culture using reference substrates and inhibitors, then prospectively compare cellular delivery, barrier integrity and kidney-injury responses.
Ma et al., proximal tubule-on-chip study (2024) ↗
Research approach. I would begin with one specified renal disease and cell population rather than a universal kidney-targeting nanoparticle. Appropriate modalities might include a small molecule, siRNA, a conjugate, an engineered lipid or polymer carrier, or eventually mRNA/circular RNA if the intended biology requires protein production. For RNA-based systems, fluorescence in renal tissue would be a distribution observation; intracellular RNA integrity, target silencing or protein expression, dose-response and renal functional outcomes would determine whether delivery actually worked.
I am particularly interested in molecular pharmaceutics and chemical biology, but an excellent project in synthesis, polymer science, experimental physiology, instrumentation or computational modelling could address just one of these questions. The target, experimental system and methods should be chosen with the laboratory, not declared in advance.
Read the extended research agenda: proposed experiments, emerging approaches and model validation
FOUR PROBLEMS / TESTABLE BEGINNINGS
Where I would put the first experiments.
Each proposal begins with a problem that can be tested. The target, model and methods would be refined with the research group, whether the outcome is a clearer mechanism, a new material, a measurement technique or a therapeutic approach.
01 STATE-DEPENDENT TARGETS
Can the vulnerable state be identified before choosing the drug?
Take a signalling interaction involved in pathological repair or tumour invasion. Instead of ranking targets only by abundance, characterize when the relevant protein complex forms, where it resides and what changes after injury or treatment. Compare orthogonal binding measurements with genetic perturbation and functional rescue. A compound that binds convincingly but fails to alter a disease-associated state is not an encouraging hit merely because its affinity is strong. Small-molecule discovery targeting the SLIT2–ROBO axis illustrates how to connect a molecular screening programme to questions of biological function.
First possible test: time-resolved cell states, target engagement, genetic perturbation and rescue. Decisive question: does the target causally control the selected transition?
02 PRODUCTIVE EXPOSURE
Where, exactly, does a promising therapeutic disappear?
In pulmonary RNA therapy a formulation must survive aerosol generation, deposit in the relevant region, interact with lung lining fluid, enter the intended cell and release functional cargo. I would compare delivery systems across those separate barriers in healthy and injury-conditioned models, rather than rank them by size or fluorescent uptake alone. Functional knockdown, intracellular release, barrier recovery and inflammation should be measured alongside delivered dose. Experimental work showing that endosomal escape can provoke inflammatory damage makes the design problem sharper: greater entry need not produce a safer or more effective medicine.
First possible test: matched RNA cargo and administered dose across formulations, measuring aerosol mass balance, cellular delivery, knockdown and tissue function. Decisive question: which barrier limits productive and tolerable exposure?
03 SPATIAL RESPONSE
Why does recognition fail to become treatment?
Antibody–drug conjugates, aptamer conjugates and engineered T cells make recognition central to therapeutic design, but recognition is only the beginning. In an ADC, antigen heterogeneity, internalization, linker cleavage, payload release and bystander toxicity matter. In aptamer-directed carriers the questions include nuclease stability, receptor-dependent uptake and intracellular escape; aptamer-guided oligonucleotide delivery to muscle stem cells is a useful research example. CAR-T and other engineered immune cells confront trafficking, persistence and suppressive microenvironments; tumour-on-chip research on CAR-T action illustrates a possible experimental route. These modalities share questions about access and spatial response, not interchangeable measures of success.
First possible test: one modality and one co-culture or tissue chip, with mapped recognition sites and time-resolved functional response. Decisive question: does failure arise from recognition, access, release or tissue resistance?
04 PROSPECTIVE PREDICTION
Can a model predict the next experiment before seeing it?
I would first model a tractable sequence: formulation characteristics, delivered dose, tissue transport, intracellular disposition and measured pharmacological response. Mechanistic transport, PBPK, QSP or statistical models could each be used where their parameters are identifiable. I would specify predictions before collecting a held-out dataset and assess calibration and systematic error afterward. The model should be able to recommend an informative experiment, and it should also be allowed to fail.
First possible test: a modular inhalation-to-cell-response model linked to aerosol and epithelial-barrier experiments. Decisive question: does it predict unseen results better than a simpler baseline, and where does its uncertainty become unacceptable?
FRONTIERS / A LONGER HORIZON
The field is changing at its boundaries.
I am interested in induced-proximity pharmacology, including targeted degradation and compounds that recruit proteins into functional complexes. Here, measuring binary affinity may be insufficient: ternary-complex geometry, cooperativity and the downstream cellular event become critical. The emerging induced-proximity therapeutic landscape broadens possible targets while making selectivity, cellular access and target-specific functional evidence more important.
Programmable recognition presents another frontier. Aptamers, antibody conjugates, RNA cargoes, logic-gated cellular therapies and responsive materials can make an intervention conditional on its environment. Each architecture creates failure modes of its own, including unstable conjugates, intracellular trapping, antigen escape and on-target injury in normal tissue. In solid tumours, the microenvironment can obstruct engineered immune-cell trafficking and persistence; engineering the therapeutic cell therefore raises questions about the experimental environment used to test it.
I would like to follow the earliest measurable stages of epithelial injury and abnormal repair in longitudinal human-derived models. The experimental design would require controlled mechanical exposure, repeated functional measurements and perturbations capable of distinguishing a reversible response from progressive damage. A human inflammatory-bowel-disease-on-chip investigation demonstrates how disease progression and cancer initiation can be approached experimentally. A measured transition still has to be shown to be causal, not merely correlated with disease.
Finally, the connections between tissue engineering and pharmacometrics are becoming more interesting. Multi-organ tissue-chip research highlights the challenge of preserving the biological niche of each tissue while permitting controlled exchange between them. These systems are not substitutes for human physiology by declaration. Their future value depends on quantitatively linking their readouts to drug transport, model assumptions and independent reference evidence.
A LONG-TERM LABORATORY IDEA
Make the evidence travel with the medicine.
My ideal scientific environment would allow a therapeutic candidate to be interrogated at successive scales without pretending that one researcher should master every instrument. A medicinal-chemistry or chemical-biology question would inform the molecule, conjugate or cell. Formulation and transport experiments would show whether it reaches the intended site in active form. A tissue-engineering experiment would measure what happens in a defined human disease state. Quantitative pharmacology would then ask how far the result can reasonably be extrapolated.
One plausible beginning is pulmonary disease, where my experience with epithelial injury and inhalable formulations provides a concrete entry point. The methods can expand through controlled-release science, cancer chemical biology, engineered immune therapeutics and patient-relevant pharmacometrics. The ambition is portable, but the first biological model must be specific. Over time, I would like to develop open, versioned datasets, reproducible protocols, qualified tissue models and modular mechanistic software. The most important output would be a better decision about which therapeutic hypothesis deserves the next experiment.
Conversations and scientific work that attracted me to different researchers point toward aerosol physics, intracellular RNA delivery, ocular controlled-release systems, molecular recognition, cellular engineering and human variability in drug exposure. They are not the same specialism. I want to learn their distinct reasoning and create experiments in which one discipline can genuinely test another's assumptions.
SELECTED LITERATURE / SCIENTIFIC CONTEXT
Evidence informing the horizon.
I use the studies below to identify experiments worth repeating, design choices worth challenging and measurements that might resolve disagreements between models. Each is linked to its original publication and authors.
Ryan and colleagues · Nebulised RNA delivery from aerosol to cytosol (review, 2024) ↗
Cellular and biophysical barriers to LNP-mediated cytosolic RNA delivery (original research, 2025) ↗
Endosomal membrane damage and inflammation in LNP delivery (original research, 2025) ↗
Aptamer–nanoparticle delivery to muscle stem cells (original research, 2025) ↗
Tumour-on-chip for engineered CAR-T immunotherapy (original research, 2025) ↗
Human IBD-on-chip and early disease progression (original research, 2026) ↗
Quantitative standards for microphysiological systems (perspective, 2024) ↗
Multi-organ tissue-chip niches and ADMET prediction (review, 2026) ↗
Induced-proximity therapeutic modalities (review, 2025/2026) ↗
FDA · General considerations for NAM validation (draft guidance, March 2026) ↗
EVIDENCE AND LIMITS / 05
What would make the model useful?
Before using an organoid or computational model to rank candidate therapies, I would define its context of use and compare its performance against an appropriate reference. Maturity of the biological model, reproducibility across preparations and prospective predictive accuracy would determine whether additional complexity is useful.
Work on human tissue chips and new approach methodologies provides a serious basis for reducing animal experimentation in suitable applications, but replacement cannot be asserted universally. My ambition is to develop and evaluate models that are informative for the biological decision at hand, and to change the model when the evidence requires it.
The original longer research essay ↗ remains available, including its earlier research history and reading list. Two worked doctoral experiments ↗ illustrate how a broader direction can become a falsifiable study.