AIMS:Choice of first-in-human dose has critical implications for the safety of Phase I participants as well as the likelihood of reaching the therapeutic dose range during escalation. In this analysis, we present a population concentration-response modelling approach for selecting the Phase I starting dose for a novel stimulator of interferon response cGAMP interactor 1 (STING) agonist, SNX281. METHODS:Given the immune agonist mechanism of SNX281, we opted to select the starting dose according to the minimum anticipated biological effect level (MABEL). To determine the MABEL concentration, we fitted a population concentration-response model to cytokine induction data from an ex vivo whole blood assay. We selected a whole blood assay to obviate the need for free fraction scaling for this highly protein-bound drug. We used the population concentration-response model to estimate the lower 10th percentile for the 10% maximal interferon-β response concentration of SNX281, which was chosen as the MABEL concentration. We translated the ex vivo MABEL concentration to a human MABEL dose using a human pharmacokinetic projection based on allometric scaling from preclinical species. RESULTS:The human dose-peak concentration relationship projection fell within 2-fold of the clinical result. After the application of a safety factor, the MABEL dose was applied in the clinic and did not demonstrate dose-limiting toxicities. CONCLUSIONS:Our novel population modelling-based MABEL strategy for first-in-human dose selection resulted in successful clinical translation of a small molecule STING agonist.

In a tumor undergoing evolution under treatment, two forces drive changes in the frequencies of genetically distinct subpopulations (subclones): drift and natural selection. To identify the presence of a subclone with a fitness advantage in a tumor undergoing treatment (i.e. a drug-resistant subclone), it is important to distinguish between these two forces. In this work, we use simulations of subclonal dynamics to propose a method of determining and quantifying the relative fitness of a subclone, given a change in its population fraction. To simulate subclonal dynamics, a Gillespie algorithm was used to model the stochastic birth and death events of a tumor with six subpopulations of initially equal size. We simulated subclonal dynamics under two conditions – one in which all subclones were drifting (“drift simulations”) and the other in which one subclone was resistant to treatment (“selection simulations”). In drift simulations, the growth and death rates of all subpopulations were set the same, while in selection simulations, the growth rate of only one subpopulation was increased. In each type of simulation, the distribution of the size of the largest subclone was recorded over time. From these distributions, we defined a time-dependent threshold of subpopulation size, for determining if a subpopulation had a significant fitness advantage. Selecting various thresholds of subpopulation size led to receiver operating characteristic (ROC) curves showing the accuracy of this method in identifying a subclone with a known fitness value. Furthermore, by assuming a normal distribution of a priori fitness advantages, a Bayesian estimator was formulated to predict the most probable fitness value. In our simulations of subclonal dynamics, we found that in selection simulations the fitter subclone tended to quickly and robustly sweep to dominance in the tumor population (“fixate”). In drift simulations as well, one subclone would typically fixate but only after a longer period of time. Consistent with these observations, we found that at intermediate times the size distributions of the largest subclone fraction for drift versus selection simulations diverge. From this, we were able to choose thresholds in subpopulation size for accurately assessing the existence of a fitness advantage. For a subclone with a 15% growth advantage, this method could determine the significance of its relative fitness with greater than 90% sensitivity and specificity. In addition, we used this method to quantify a subclone’s fitness advantage as a roughly linear relationship to the increase in its subpopulation size over a given time. In this work, we demonstrate a method for inferring the presence of natural selection and the fitness advantage of a resistant subclone from changes in subclonal frequencies over time. Applications of this method to Next-Generation Sequencing data may allow the early identification of aggressive and/or resistant subclones in cancers. Michael Salazar, Andrew Chen, Madison Stoddard, T. Ryan Gregory, Arijit Chakravarty. Using subclonal dynamics to detect and quantify fitness advantages of resistant subclones in tumors under treatment [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr B102.

OBJECTIVES: In this study, we have examined the accuracy of a range of estimation methods for finding the maximum tolerated dose (MTD) in a clinical trial. A popular approach is to use 3+3 dose escalation, where three patients at a time are dosed in each cohort and the MTD occurs when at least two patients experience dose limiting toxicity (DLT). In practice, toxicities are often recorded on a graded scale, MTD estimation often requires dichotomizing this graded data to a binary measure of dose limiting toxicity (DLT). Estimation algorithms that retain graded data and estimate MTD by modeling patient response heterogeneity may reduce information loss and improve MTD estimation accuracy, but these strategies have yet to be comprehensively tested. Here we put forward a novel approach for MTD estimation, the Population Response Estimate (PRE), and compare its performance to other approaches such as ordinal logistic regression, logistic regression, and 3+3. METHODS: Our approach, PRE, applies Maximum Likelihood Theory to graded toxicity data to estimate the toxicity central tendency curve and population heterogeneity, which in turn is used to construct a probability model for patient DLT. We evaluated the performance of MTD estimation algorithms by simulating synthetic clinical toxicity data. This was done by randomly generating patient toxicity samples from an assumed-true underlying toxicity relationship relating drug dose to exposure, and drug exposure to patient toxicity (based on a first-order Hill function describing the central tendency combined with a population response heterogeneity/probability density modeled as a Gaussian distribution). The true MTD (MTDTRUE) of the is defined at the drug exposure that causes 25% of the patient population to experience DLT (toxicity of grade 3 or higher). We evaluated each MTD estimation approach for its ability to estimate MTDTRUE on the same dataset, containing 5000 simulated curves for each toxicity function examined. We used both 3+3 dose escalation as well as an alternative paradigm (adaptive dose escalation) to evaluate each method. RESULTS: In this dataset, we found that PRE performs best in estimating MTDTRUE in both dose-ranging paradigms and all true toxicity relationships examined, providing the best estimate for MTDTRUE in 41% of the cases (vs 22% for 3+3, 22% for ordinal logistic regression and 18% for binary logistic regression) . We found that all tested MTE estimation approaches, including PRE, tend to strongly underestimate MTE for asymptotic exposure-toxicity relationships when 3+3 dose escalation is used. The use of the alternative dose-escalation paradigm (adaptive dose escalation) improves the accuracy of PRE as well as the other methods. CONCLUSIONS: The traditional 3+3 dose escalation scheme possesses weaknesses in both the escalation and estimation algorithms. The use of adaptive dose escalation coupled with better MTD estimation algorithms (such as PRE) can improve MTD estimation. Citations: No citations listed.

Orthopoxviruses can transmit via inhalation of virus-laden airborne particulates, with the initial infection triggered along the respiratory pathway. Understanding the flow physics of inhaled aerosols and droplets within the respiratory tract is crucial for improving transmission mitigation strategies and elucidating disease pathology. Here, we introduce an experimentally-validated physiological fluid dynamics model simulating inhaled onset of smallpox caused by the variola virus of Orthopoxvirus genus. Using high-fidelity Large Eddy Simulations, we modeled inhaled airflow and particulate motion within anatomical airway domains reconstructed from medical imaging. By integrating these simulations with viral concentration and individual immune factors, we estimated the critical exposure durations for infection onset to be between 1−19 hours, aligning with existing smallpox literature. To formalize the broader applicability of this framework, we extended our analysis to mpox virus, a circulating pathogen from the same genus. For mpox, the mechanophysiological computations indicate a typical critical exposure duration of 24−40 hours; however, this can vary significantly–from as short as 8 hours to as long as 127 hours–depending on virion concentration fluctuations within inhaled particulates, assuming happenstance of viral evolution. Predictably longer than the critical exposure durations for smallpox, the mpox findings still strongly suggest the possibility for airborne inhaled transmission during prolonged proximity.

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2’s propensity for asymptomatic transmission, raise the question “how reliable was contact tracing for COVID-19 in the United States”? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

Late-stage clinical trial failures increase the overall cost and risk of bringing new drugs to market. Determining the pharmacokinetic (PK) drivers of toxicity and efficacy in preclinical studies and early clinical trials supports quantitative optimization of drug schedule and dose through computational modeling. Additionally, this approach permits prioritization of lead candidates with better PK properties early in development. Mylotarg is an antibody-drug conjugate (ADC) that attained U.S. Food and Drug Administration (FDA) approval under a fractionated dosing schedule after 17 years of clinical trials, including a 10-year period on the market resulting in hundreds of fatal adverse events. Although ADCs are often considered lower risk for toxicity due to their targeted nature, off-target activity and liberated payload can still constrain dosing and drive clinical failure. Under its original schedule, Mylotarg was dosed infrequently at high levels, which is typical for ADCs because of their long half-lives. However, our PK modeling suggests that these regimens increase maximum plasma concentration (Cmax)-related toxicities while producing suboptimal exposures to the target receptor. Our analysis demonstrates that the benefits of dose fractionation for Mylotarg tolerability should have been obvious early in the drug's clinical development and could have curtailed the proliferation of ineffective Phase III studies. We also identify schedules likely to be even more efficacious without compromising on tolerability. Alternatively, a longer-circulating Mylotarg formulation could obviate the need for dose fractionation, allowing superior patient convenience. Early-stage PK optimization through quantitative modeling methods can accelerate clinical development and prevent late-stage failures.

In the fourth year of the COVID-19 pandemic, public health authorities worldwide have adopted a strategy of learning to live with SARS-CoV-2. This has involved the removal of measures for limiting viral spread, resulting in a large burden of recurrent SARS-CoV-2 infections. Crucial for managing this burden is the concept of the so-called wall of hybrid immunity, through repeated reinfections and vaccine boosters, to reduce the risk of severe disease and death. Protection against both infection and severe disease is provided by the induction of neutralizing antibodies (nAbs) against SARS-CoV-2. However, pharmacokinetic (PK) waning and rapid viral evolution both degrade nAb binding titers. The recent emergence of variants with strongly immune evasive potential against both the vaccinal and natural immune responses raises the question of whether the wall of population-level immunity can be maintained in the face of large jumps in nAb binding potency. Here we use an agent-based simulation to address this question. Our findings suggest large jumps in viral evolution may cause failure of population immunity resulting in sudden increases in mortality. As a rise in mortality will only become apparent in the weeks following a wave of disease, reactive public health strategies will not be able to provide meaningful risk mitigation. Learning to live with the virus could thus lead to large death tolls with very little warning. Our work points to the importance of proactive management strategies for the ongoing pandemic, and to the need for multifactorial approaches to COVID-19 disease control.

Supplementary Figure 2 from Phase I Assessment of New Mechanism-Based Pharmacodynamic Biomarkers for MLN8054, a Small-Molecule Inhibitor of Aurora A Kinase

SARS-CoV-2 vaccinations were initially shown to substantially reduce risk of severe disease and death. However, pharmacokinetic (PK) waning and rapid viral evolution degrade neutralizing antibody (nAb) binding titers, causing loss of vaccinal protection. Additionally, there is inter-individual heterogeneity in the strength and durability of the vaccinal nAb response. Here, we propose a personalized booster strategy as a potential solution to this problem. Our model-based approach incorporates inter-individual heterogeneity in nAb response to primary SARS-CoV-2 vaccination into a pharmacokinetic/pharmacodynamic (PK/PD) model to project population-level heterogeneity in vaccinal protection. We further examine the impact of evolutionary immune evasion on vaccinal protection over time based on variant fold reduction in nAb potency. Our findings suggest viral evolution will decrease the effectiveness of vaccinal protection against severe disease, especially for individuals with a less durable immune response. More frequent boosting may restore vaccinal protection for individuals with a weaker immune response. Our analysis shows that the ECLIA RBD binding assay strongly predicts neutralization of sequence-matched pseudoviruses. This may be a useful tool for rapidly assessing individual immune protection. Our work suggests vaccinal protection against severe disease is not assured and identifies a potential path forward for reducing risk to immunologically vulnerable individuals.

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral “spike” proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as “spike” vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future.

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17 μ m are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude.

The recently emerged SARS-CoV-2 virus has led to a prolonged pandemic characterized by ongoing viral evolution. Vaccines have been an important piece in the strategy to combat the virus but have been insufficient to contain it as the virus continues to evolve to evade immunity developed by vaccination and infection. A consistent argument is that vaccination or prior immunity will lead to less severe infections. In this review, we address the question of whether the virus can evolve to become more virulent, despite prior infection. We describe the intrinsic characteristics of the virus and their relationship to altered virulence. We show that it is likely that viral evolution is subject to evolutionary drift, and it cannot be assumed that the virus will necessarily evolve to be less virulent, or that prior immunity will offer durable protection against severe disease. This has strong implications for public health strategies to confront the ongoing challenges presented by SARS-CoV-2 and implies that there are significant risks to a strategy based on the assumption of waning virulence.

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-spike protein-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds have previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

As the COVID-19 pandemic progresses, widespread community transmission of SARS-CoV-2 has ushered in a volatile era of viral immune evasion rather than the much-heralded stability of "endemicity" or "herd immunity." At this point, an array of viral strains has rendered essentially all monoclonal antibody therapeutics obsolete and strongly undermined the impact of vaccinal immunity on SARS-CoV-2 transmission. In this work, we demonstrate that antibody escape resulting in evasion of pre-existing immunity is highly evolutionarily favored and likely to cause waves of short-term transmission. In the long-term, invading strains that induce weak cross-immunity against pre-existing strains may co-circulate with those pre-existing strains. This would result in the formation of serotypes that increase disease burden, complicate SARS-CoV-2 control, and raise the potential for increases in viral virulence. Less durable immunity does not drive positive selection as a trait, but such strains may transmit at high levels if they establish. Overall, our results draw attention to the importance of inter-strain cross-immunity as a driver of transmission trends and the importance of early immune evasion data to predict the trajectory of the pandemic.

The basic reproductive number (R 0 ) and superspreading potential ( k ) are key epidemiological parameters that inform our understanding of a disease’s transmission. Often these values are estimated using the data obtained from contact tracing studies. Here we performed a simulation study to understand how incomplete data due to preferential contact tracing impacted the accuracy and inferences about the transmission of SARS-CoV-2. Our results indicate that as the number of positive contacts traced decreases, our estimates of R 0 tend to decrease and our estimates of k tend to increase. Notably, when there are large amounts of positive contacts missed in the tracing process, we can conclude that there is no indication of superspreading even if we know there is. The results of this study highlight the need for a unified public health response to transmissible diseases.