The Delivery Vector Shapes Immunity: Causal Engineering of Bionanotechnology, Non-Invasive Drug and Gene Delivery, Nanomedicine

Immune consequences of bionanotechnology delivery vectors is shifting from basic potency and delivery or association to the importance of understanding the why and when of a result. A mechanistic network of particle composition, size, surface chemistry, protein corona, cellular uptake, innate sensing, and trafficking is at the core of this transition. This network is important because a carrier that is only used for carrying payloads can tune up the inflammation, antigen presentation, complement activation or change or tissue distribution. The more robust development strategy thus considers mechanism, context and uncertainty all as intertwined design variables in one process rather than being distinct processes carried out at various stages.

The study of nanomedicine has also shown that an active delivery vector exists well before than payload delivery is completed. The composition and size of particles have an impact on organ distribution, and surface chemistry and adsorbed proteins impact on complement activation, phagocyte recognition, tissue uptake, endosomal trafficking and inflammatory signaling. Different environments and different therapeutic results can thus be created by even carrying equal loads. Matched controls, varied vector attributes one by one, are the requirement of causal engineering, a need that cannot be met by vague claims of an immune consequence of "payload".

The impact is a new paradigm for discovery, based on comparative vector libraries, cytokine profiling, complement assays, single-cell immune phenotyping, biodistribution imaging and matched payload controls. None of these assays is game changers on its own because each assay only measures a subset of the larger biological/clinical process. Independent measurements that all converge on the same causal story, over dose, time, model system, and context of a given biological system, increase confidence. Even when discordant results are found, the results are useful because they can be another indicator of an underlying mechanism, an assay artifact, population differences, or limitation of exposure, which are worth investigating before expensive development efforts are wasted.

When making a decision, the question is what vector parameters are required to deliver the desired therapeutic efficacy, but at the same time not promote an unwanted immune response. A definitive answer to this question requires explicit and clear comparison to a "best estimate" of possible alternatives such as off target effects, parallel pathways, differential exposure, or measurement bias. The removal, blocking or restoration of a proposed component in an experiment is particularly informative which is referred to as a perturbation experiment. Therefore, the confidence in the mechanism is from challenge tests and not from repeated confirmation within one favorable assay.

Last but not least, nearly all results in the field of immune consequences of bionanotechnology delivery vectors depend on the context. Relevant variation encompasses organ function, disease state, age and inflammatory status as well as pre-existing immunity. A good intervention with one set of these conditions may not be as effective, more toxic or result in different biological response with another set of conditions. Stratification is more useful when based on variables related to mechanism or exposure, rather than just descriptions categories, which provide little insight into the reasons for changes in treatment performance.

The same importance should be given to risk assessment due to innate immune activation, anti-vector responses, off-target organ exposure, dose accumulation, and species-specific immune behavior. Early safety work has been more successful when associated with the same mechanistic paradigm as efficacy and unwanted effects can be linked to target biology, carrier behavior, exposure, metabolites or analytical bias. This way, the risk of safety being the last thing on the checklist and not connected to the discovery process is minimized. It also provides more transparent trade-offs on how to achieve greater effectiveness from increased exposure, a wider modulation of pathways, or the more extended persistence in a sensitive tissue.

Another layer is added due to the options of non-invasive gene and drug delivery that provides predictable tissue access and manageable immunogenicity, which is referred to as translational development. Preclinical success is more informative if experiments are conducted under clinically relevant conditions with relevant concentration ranges, biological barriers, treatment history and sources of biological heterogeneities. It is less useful to be able to go from a convenient model to a complex one, than to have a series of models that hopefully answer the questions of uncertainty. Therefore for each step, there must be a clear decision that will be made: to continue, to redesign, to narrow the target population, to change dosing, or to gather another sort of mechanistic information.

Two points are important, manufacturing and analytical control: the former because batch consistency is fundamental for the biological activity, the latter because particle morphology, encapsulation, surface composition, sterility and storage stability have a direct impact on the biological activity. The more the material in the test changes from batch to batch or the less well characterized the assay inputs, the less value there is to a biological explanation. Therefore critical attributes must have specific ranges, be sensitive to measure and link process conditions to biological performance. This discipline is particularly relevant for complex molecules, nanomaterials, biomaterials, and studies involving large amounts of data, which may result in very small changes to exposure, signaling, and/or response to treatment.

The immune consequences of bionanotechnology delivery vectors are also better served by having a higher degree of separation between prediction and confirmation for data analysis. Model output is not the same as biological truth but can rank hypotheses, integrate high dimensional measurements, and reveal uncertainty, via computational models, machine learning, causal estimators, and mechanistic simulations. Useful models include stating assumptions, quantifying calibration, identifying area with weak support, and pointing to area which could yield information to alter a decision for the next experiment. It is important to also validate results in the future, as patterns that are best fit in the past might not be best fit in the future.

Another significant change relates to time as many of the processes underlying the effects of bionanotechnology delivery vectors on immunity will occur after the initial exposure. Even if there is a single endpoint that is favorable, the early response, adaptation, clearance, immune programming, tissue remodeling, adherence, and/or resistance may take different trajectories. Longitudinal measurement determines if the intervention has an effect that is stable, upheaval, or liability. Another advantage of time-resolved data is that it can enhance the causal interpretation of the proposed mechanism, since it will appear prior to the downstream phenotype, not afterward.

The more efficient the experimental design, the more efficient the study will be when associated with a concrete uncertainty budget. The properties of the particles that can be modified either by particle composition, size, surface chemistry, protein corona, cell uptake or intracellular trafficking, or by other properties of the particles, can be ranked by their ability to alter the desired delivery profile instead of creating an unintended immune program, and not by novelty. Replication then occurs across complementary models to determine if the conclusion is dependent on any one of the three factors: an assay, a laboratory condition, or a population. Such discipline would preclude wasted optimization and informative failures as each failure reduces the design space of what may be happening and the next design iteration would be more successful.

It is also important that claims are traceable to measurements in order to build regulatory and clinical credibility. When immune consequences of bionanotechnology delivery vectors are concerned, a development package gets stronger when the raw observations, processing decisions, model assumptions, protocol deviations, and material attributes are all linked as early studies connect with translational studies. That traceability can be helpful to differentiate real biological variation from technical noise, and aid in subsequent safety or efficacy testing. It also provides a more logical framework to revise conclusions if new data change an assumption.

Communication in this domain must be more specific, too. The terms targeted, selective, durable, safe, predictive, or personalized alone don't have much meaning without including a measurement, a comparator, a population, and an uncertainty range. Having negative reports is more useful and avoids having false-positive findings reported as mechanisms. Such precision is a common language to determine if a claim is based on direct measurement or calibrated inference or on an untested assumption for researchers, developers, clinicians and regulators.

Chemistry or formulation, mechanistic measurement, quantitative modeling, safety, manufacturing control, and clinically relevant context, therefore, is the strongest direction for immune consequences of the delivery vectors of bionanotechnology, rather than any single new technology. The rate of progress increases if failed assumptions are uncovered early, and successful mechanisms are the subject of conscious testing with difficulty. This rationale is used to assign the delivery vector to be an active pharmaceutical ingredient. The key innovation that is of most value overall is a development system that learns from each experiment it conducts while maintaining a focus on causal interpretation, reproducibility, and real world relevance throughout each experiment.