Youfang Cao

Lecanemab (Leqembi®) was recently approved by health authorities in the United States, Japan, and China to treat early Alzheimer's disease (AD), including patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease upon confirmation of amyloid beta pathology. Extensively and sparsely sampled PK profiles from 1619 AD subjects and 21,929 serum lecanemab observations from two phase I, one phase II, and one phase III studies were well characterized using a two‐compartment model with first‐order elimination. The final PK model quantified covariate effects of body weight and sex on clearance and central volume of distribution, ADA‐positive status, and albumin on clearance, and of Japanese ethnicity on central and peripheral volumes of distribution. Exposure to lecanemab was comparable between two lecanemab‐manufacturing processes. However, none of the identified covariates in the model had a clinically relevant impact on model‐predicted lecanemab C_max or AUC at steady state following 10 mg/kg bi‐weekly. Importantly, age, a well‐recognized risk factor for AD, was not found to significantly affect lecanemab PK. The incidence of ARIA‐E as a function of lecanemab exposure was modeled using a logit function with data pooled from 2641 subjects from the phase II and phase III studies, in which a total of 177 incidences of ARIA‐E were observed. The probability of ARIA‐E was significantly correlated with model‐predicted C_max and predicted to be higher in subjects homozygous for APOE4. The incidence of isolated ARIA‐H was not associated with lecanemab exposure and was similar between placebo and lecanemab‐treated subjects.

Background: Clinical efficacy of lecanemab was confirmed in a Phase 3 trial (Clarity AD, NCT03887455), reducing markers of amyloid in early Alzheimer’s disease and slowing cognitive and functional decline as compared to placebo. However, some subjects experienced amyloid‐related imaging abnormalities (ARIA) associated with edema/effusion (ARIA‐E) or hemorrhage (ARIA‐H). PK/PD analyses were performed to explore the potential relationship between ARIA‐E and isolated ARIA‐H incidences with lecanemab exposure and key covariates.Method: The Clarity AD trial randomized 1795 participants to receive lecanemab (N = 898, 10 mg/kg bi‐weekly) or placebo (N = 897). A total of 177 ARIA‐E incidences were observed from pooled Phase 2 (NCT01767311) and Phase 3 (Clarity AD) data. Additionally, a total of 150 participants from Clarity AD trial experienced treatment‐emergent isolated ARIA‐H (an ARIA‐H event without concurrent ARIA‐E). The ARIA‐E incidence rate was modeled as a function of lecanemab steady state exposures, and effects of potential covariates were explored. Graphical PK/PD analysis was performed to examine the relationship between isolated ARIA‐H and lecanemab steady‐state exposures.Result: The observed ARIA‐E incidence was best modeled using lecanemab maximum concentration at steady‐state (Css,max) with ApoE4 genotype identified as a significant covariate. The model indicates a higher ARIA‐E incidence in homozygous carriers compared to non‐homozygous carriers or noncarriers (p<0.0001). At mean lecanemab Css,max = 305 µg/ml in subjects receiving 10 mg/kg bi‐weekly in Clarity AD, the model predicted a 5.45% (95% CI: 3.75% ‐ 7.84%) incidence of ARIA‐E in noncarriers, 9.85% (95% CI: 7.96% ‐ 12.1%) in heterozygous, and 28.0% (95% CI: 22.6% ‐ 34.1%) in homozygous carriers. No other covariate explored (age, sex, race, baseline MMSE, or occurrence of anti‐drug antibodies) had a statistically significant effect on ARIA‐E incidence. For isolated ARIA‐H, the graphical analysis identified no apparent relationship between isolated ARIA‐H incidence with lecanemab exposure. Isolated ARIA‐H incidence was associated with ApoE4 genotype, with homozygous having the highest isolated ARIA‐H incidence.Conclusion: The incidence of ARIA‐E increased with lecanemab Css,max. The incidence rate of isolated ARIA‐H was demonstrated independent of lecanemab exposure. The ApoE4 genotype impacts both ARIA‐E and isolated ARIA‐H, with increased incidences in homozygous carriers compared to heterozygous carriers or non‐carriers.

Coronavirus disease 2019 (COVID‐19) global pandemic is caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS‐CoV‐2 is critical for development of effective treatments. An Immune‐Viral Dynamics Model (IVDM) is developed to describe SARS‐CoV‐2 viral dynamics and COVID‐19 disease progression. A dataset of 60 individual patients with COVID‐19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS‐CoV‐2, viral‐induced cell death, and time‐dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed‐effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell‐based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose‐efficacy response analysis for COVID‐19 drug development.

Treating macaques with an anti-α4β7 antibody under the umbrella of combination antiretroviral therapy (cART) during early SIV infection can lead to viral remission, with viral loads maintained at < 50 SIV RNA copies/ml after removal of all treatment in a subset of animals. Depletion of CD8⁺lymphocytes in controllers resulted in transient recrudescence of viremia, suggesting that the combination of cART and anti-α4β7 antibody treatment led to a state where ongoing immune responses kept the virus undetectable in the absence of treatment. A previous mathematical model of HIV infection and cART incorporates immune effector cell responses and exhibits the property of two different viral load set-points. While the lower set-point could correspond to the attainment of long-term viral remission, attaining the higher set-point may be the result of viral rebound. Here we expand that model to include possible mechanisms of action of an anti-α4β7 antibody operating in these treated animals. We show that the model can fit the longitudinal viral load data from both IgG control and anti-α4β7 antibody treated macaques, suggesting explanations for the viral control associated with cART and an anti-α4β7 antibody treatment. This effective perturbation to the virus-host interaction can also explain observations in other nonhuman primate experiments in which cART and immunotherapy have led to post-treatment control or resetting of the viral load set-point. Interestingly, because the viral kinetics in the various treated animals differed—some animals exhibited large fluctuations in viral load after cART cessation—the model suggests that anti-α4β7 treatment could act by different primary mechanisms in different animals and still lead to post-treatment viral control. This outcome is nonetheless in accordance with a model with two stable viral load set-points, in which therapy can perturb the system from one set-point to a lower one through different biological mechanisms.

Significance: The reservoir of HIV latently infected cells is the major obstacle for complete eradication of HIV infection. Latency and its transactivation in HIV-infected cells are controlled by the intracellular HIV Tat gene circuit. By reconstructing the probability landscape of the circuit through exact solution of the underlying chemical master equation we examined the detailed mechanism of probabilistic intracellular control of latency and transactivation. We show that while the Tat circuit lacks deterministic bistability its probability landscape exhibits bimodality. Moreover, by exploring changes in the probability landscapes under different perturbations, our study suggests effective therapeutic targets for strategies of “shock and kill” to eliminate latently infected cells and “block and lock” to enforce deep latency.

CD8⁺ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection. However, both the extent to which and the mechanisms by which CD8⁺ lymphocytes contribute to viral control are not completely understood. A recent experiment depleted CD8⁺ lymphocytes in simian immunodeficiency virus (SIV)-infected rhesus macaques (RMs) on antiretroviral treatment (ART) to study the role of CD8⁺ lymphocytes. CD8⁺ lymphocytes depletion resulted in temporary plasma viremia in all studied RMs. Viral control was restored when CD8⁺ lymphocytes repopulated. We developed a viral dynamic model to fit the viral load (VL) data from the CD8 depletion experiment. We explicitly modeled the dynamics of the latent reservoir and the SIV-specific effector cell population including their exhaustion and their potential cytolytic and noncytolytic functions. We found that the latent reservoir significantly contributes to the size of the peak VL after CD8 depletion, while drug efficacy plays a lesser role. Our model suggests that the overall CD8⁺ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion. Based on estimated parameters, our model suggests that before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion. However, after the start of ART, the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells. Further, we estimate that the cytolytic killing rate can be significantly larger than the cytopathic death rate in some animals during the second phase of ART-induced viral decay. Lastly, our model provides a new explanation for the puzzling findings by Klatt et al. and Wong et al. that CD8 depletion done immediately before ART has no noticeable effect on the first phase viral decay slope seen after ART initiation Overall, by incorporating effector cells and their exhaustion, our model can explain the effects of CD8 depletion on VL during ART, reveals a detailed dynamic role of CD8⁺ lymphocytes in controlling viral infection, and provides a unified explanation for CD8 depletion experimental data. CD8⁺ lymphocytes play an important role in suppressing in vivo viral replication in HIV infection. However, both the extent to which and the mechanisms by which CD8⁺ lymphocytes contribute to viral control are not completely understood. By mathematically modeling data from a recent CD8 depletion experiment done in antiretroviral (ART) treated animals, our results suggest that the overall CD8⁺ lymphocyte cytolytic killing rate is dynamically changing depending on the levels of antigen-induced effector cell activation and exhaustion, i.e. before ART or without ART the overall CD8 cytolytic killing rate is small due to exhaustion. However, after the start of ART, the overall CD8 cytolytic killing rate increases due to an expansion of SIV-specific CD8 effector cells. By incorporating effector cells and their exhaustion, our model explains the effects on viral load of CD8 depletion done before ART or during ART, reveals a detailed dynamic role of CD8⁺ lymphocytes in controlling viral infection, and provides a unified explanation for CD8 depletion experimental data.

Persistence of HIV is attributed primarily to latent infection of CD4+ T cells. Honeycutt et al. report that in humanized mice lacking T cells HIV can rebound from myeloid cells after antiretroviral treatment interruption, suggesting that persistence of HIV could involve other cell types. Despite years of fully suppressive antiretroviral therapy (ART), HIV persists in its hosts and is never eradicated. One major barrier to eradication is that the virus infects multiple cell types that may individually contribute to HIV persistence. Tissue macrophages are critical contributors to HIV pathogenesis1,2,3; however, their specific role in HIV persistence during long-term suppressive ART has not been established4,5,6. Using humanized myeloid-only mice (MoM), we demonstrate that HIV infection of tissue macrophages is rapidly suppressed by ART, as reflected by a rapid drop in plasma viral load and a dramatic decrease in the levels of cell-associated viral RNA and DNA. No viral rebound was observed in the plasma of 67% of the ART-treated animals at 7 weeks after ART interruption, and no replication-competent virus was rescued from the tissue macrophages obtained from these animals. In contrast, in a subset of animals (∼33%), a delayed viral rebound was observed that is consistent with the establishment of persistent infection in tissue macrophages. These observations represent the first direct evidence, to our knowledge, of HIV persistence in tissue macrophages in vivo.

Computational modelling of cells can reveal insight into the mechanisms of the important processes of tissue development. However, current cell models have limitations and are challenged to model detailed changes in cellular shapes and physical mechanics when thousands of migrating and interacting cells need to be modelled. Here we describe a novel dynamic cellular finite-element model (DyCelFEM), which accounts for changes in cellular shapes and mechanics. It also models the full range of cell motion, from movements of individual cells to collective cell migrations. The transmission of mechanical forces regulated by intercellular adhesions and their ruptures are also accounted for. Intra-cellular protein signalling networks controlling cell behaviours are embedded in individual cells. We employ DyCelFEM to examine specific effects of biochemical and mechanical cues in regulating cell migration and proliferation, and in controlling tissue patterning using a simplified re-epithelialization model of wound tissue. Our results suggest that biochemical cues are better at guiding cell migration with improved directionality and persistence, while mechanical cues are better at coordinating collective cell migration. Overall, DyCelFEM can be used to study developmental processes when a large population of migrating cells under mechanical and biochemical controls experience complex changes in cell shapes and mechanics.

Gene regulatory networks depict the interactions between genes, proteins, and other components of the cell. These interactions often are stochastic that can influence behavior of the cells. Discrete Chemical Master Equation (dCME) provides a general framework for understanding the stochastic nature of these networks. However solving dCME is challenging due to the enormous state space, one effective approach is to study the behavior of individual modules of the stochastic network. Here we used the finite buffer dCME method and directly calculated the exact steady state probability landscape for the two stochastic networks of Single Input and Coupled Toggle Switch Modules. The first example is a switch network consisting of three genes, and the second example is a double switching network consisting of four coupled genes. Our results show complex switching behavior of these networks can be quantified.