Cellular neonatal Fc receptor recycling efficiencies can differentiate target-independent clearance mechanisms of monoclonal antibodies.

In vivo clearance mechanisms of therapeutic monoclonal antibodies (mAbs) encompass both target-mediated and target-independent processes. Two distinct determinants of overall mAb clearance largely separate of target-mediated influences are non-specific cellular endocytosis and subsequent pH-dependent mAb recycling mediated by the neonatal Fc receptor (FcRn), where inter-mAb variability in the efficiency of both processes is observed. Here, we implemented a functional cell-based FcRn recycling assay via Madin-Darby canine kidney type II cells stably co-transfected with human FcRn and its light chain ß2-microglobulin. A series of pH-dependent internalization studies using a model antibody demonstrated proper function of the human FcRn complex. We then applied our cellular assays to assess the contribution of FcRn and non-specific interactions in the cellular turnover for a panel of 8 clinically relevant mAbs exhibiting variable human pharmacokinetic behavior. Our results demonstrate that non-specific endocytosis rates, pH-dependent non-specific interactions, and engagement with FcRn all contribute to the overall recycling efficiency of therapeutic monoclonal antibodies. The predictive capacity of our assay approach was highlighted by successful identification of all mAbs within our panel possessing clearance in humans greater than 5 mL/day/kg. These results demonstrate that a combination of cell-based in vitro assays can properly resolve individual mechanisms underlying the overall in vivo recycling efficiency and non-target mediated clearance of therapeutic mAbs.

Ongoing HIV replication in lymph node sanctuary sites in treated individuals contributes to the total latent HIV at a very slow rate.

Lymph nodes (LNs) serve as a sanctuary site for HIV viruses due to the heterogeneous distribution of the antiretrovirals (ARVs) inside the LNs. There is an ongoing debate whether this represents ongoing cycles of viral replication in the LNs or merely residual virus production by latently infected cells. Previous work has claimed that the measured levels of genetic variation in proviruses sampled from the blood were inconsistent with ongoing replication. However, it is not clear what rate of variation is consistent with ongoing replication in small sanctuary sites. In this study, we used a spherically symmetric compartmental ODE model to track the HIV viral dynamics in the LN and predict the contribution of ongoing replication within the LN to the whole-body proviral pool in an ARV-suppressed person living with HIV. This model tracks the reaction-diffusion dynamics of uninfected, actively infected, and latently infected T-cells as well as free virus within the LN parenchyma and the blood, and distinguishes between latently infected cells created before ARV therapy and during ARV therapy. We simulated suppressive therapy beginning in year 5 post-infection. Each LN sanctuary site had a volume of 1 ml, and we considered cases of 1 ml, 30 ml, and 250 ml total volume, which represent a single active sanctuary site, moderate systemic involvement, and involvement of the total lymphoid tissue. Viral load in the blood rapidly dropped and remained below the limit of detection in all cases but remained high in the LN sanctuary sites. Novel latent cells increased systemically over time but very slowly, taking between 25 and 50 years to reach 5 % of the total latent pool, depending on the volume of lymphoid tissue involvement. Putative sanctuary sites in LNs are limited in volume and produce novel latent cells slowly. Assays to detect genetic drift due to such sites would require very deep sequencing if sampling only from the blood. Previous studies showing a lack of genetic drift are consistent with the expected contribution of ongoing replication in lymph node sanctuary sites.

Ongoing HIV replication in lymph node sanctuary sites in treated patients contributes to the total latent HIV at a very slow rate.

Lymph nodes (LNs) serve as a sanctuary site for HIV viruses due to the heterogeneous distribution of the antiretrovirals (ARVs) inside the LNs. There is an ongoing debate whether this represents ongoing cycles of viral replication in the LNs or merely residual virus production by latently infected cells. Previous work has claimed that the measured levels of genetic variation in proviruses sampled from the blood were inconsistent with ongoing replication. However, it is not clear what rate of variation is consistent with ongoing replication in small sanctuary sites. In this study, we used a spherically symmetric compartmental ODE model to track the HIV viral dynamics in the LN and predict the contribution of ongoing replication within the LN to the wholebody proviral pool in an ARV-suppressed patient. This model tracks the reaction-diffusion dynamics of uninfected, actively infected, and latently infected T-cells as well as free virus within the LN parenchyma and the blood, and distinguishes between latently infected cells created before ARV therapy and during ARV therapy. We simulated suppressive therapy beginning in year 5 post-infection. Each LN sanctuary site had a volume of 1 ml, and we considered cases of 1ml, 30ml, and 250ml total volume, which represent a single active sanctuary site, moderate systemic involvement, and involvement of the total lymphoid tissue. Viral load in the blood rapidly dropped and remained below the limit of detection in all cases but remained high in the LN sanctuary sites. Novel latent cells increased systemically over time but very slowly, taking between 25 and 50 years to reach 5% of the total latent pool, depending on the volume of lymphoid tissue involvement. Putative sanctuary sites in LNs are limited in volume and produce novel latent cells slowly. Assays to detect genetic drift due to such sites would require very deep sequencing if sampling only from the blood. Previous studies showing a lack of genetic drift are consistent with the expected contribution of ongoing replication in lymph node sanctuary sites.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform.

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate's volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

Accelerated Repurposing and Drug Development of Pulmonary Hypertension Therapies for COVID-19 Treatment Using an AI-Integrated Biosimulation Platform.

The COVID-19 pandemic has reached over 100 million worldwide. Due to the multi-targeted nature of the virus, it is clear that drugs providing anti-COVID-19 effects need to be developed at an accelerated rate, and a combinatorial approach may stand to be more successful than a single drug therapy. Among several targets and pathways that are under investigation, the renin-angiotensin system (RAS) and specifically angiotensin-converting enzyme (ACE), and Ca2+-mediated SARS-CoV-2 cellular entry and replication are noteworthy. A combination of ACE inhibitors and calcium channel blockers (CCBs), a critical line of therapy for pulmonary hypertension, has shown therapeutic relevance in COVID-19 when investigated independently. To that end, we conducted in silico modeling using BIOiSIM, an AI-integrated mechanistic modeling platform by utilizing known preclinical in vitro and in vivo datasets to accurately simulate systemic therapy disposition and site-of-action penetration of the CCBs and ACEi compounds to tissues implicated in COVID-19 pathogenesis.

Molecular Simulation and Statistical Learning Methods toward Predicting Drug-Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design.

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API-carrier mixture and the principal API-carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API-carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.

An Integrated Spatial Dynamics-Pharmacokinetic Model Explaining Poor Penetration of Anti-retroviral Drugs in Lymph Nodes.

Although combined anti-retroviral therapy (cART) suppresses plasma HIV viremia below the limit of detection in a majority of HIV patients, evidence is emerging that the distribution of the anti-retroviral drugs is heterogeneous in tissue. Clinical studies measuring antiretroviral drug concentrations in lymph nodes (LNs) revealed lower concentrations compared to peripheral blood levels suggesting poor drug penetration properties. Our current study is an attempt to understand this poor anti-retroviral drug penetration inside lymph node lobules through integrating known pharmacokinetic and pharmacodynamic (PK/PD) parameters of the anti-retroviral drugs into a spatial model of reaction and transport dynamics within a solid lymph node lobule. Simulated drug penetration values were compared against experimental results whenever available or matched with data that is available for other drugs in a similar class. Our integrated spatial dynamics pharmacokinetic model reproduced the experimentally observed exclusion of antivirals from lymphoid sites. The strongest predictor of drug exclusion from the lymphoid lobule, independent of drug class, was lobule size; large lobules (high inflammation) exhibited high levels of drug exclusion. PK/PD characteristics associated with poor lymphoid penetration include high cellular uptake rates and low intracellular half-lives. To determine whether this exclusion might lead to ongoing replication, target CD4+ T cell, infected CD4+ T cell, free virus, and intracellular IC50 values of anti-retroviral drugs were incorporated into the model. Notably, for median estimates of PK/PD parameters and lobule diameters consistent with low to moderate inflammation, the model predicts no ongoing viral replication, despite substantial exclusion of the drugs from the lymphoid site. Monte-Carlo studies drawn from the prior distributions of the PK/PD parameters predicts increases in site-specific HIV replication in a small fraction of the patient population for lobule diameters greater than 0.2 mm; this fraction increases as the site diameter/ inflammation level increases. The model shows that cART consisting of two nRTIs and one PI is the most likely treatment combination to support formation of a sanctuary site, a finding that is consistent with clinical observations.

Positive feedback through inflammation creates bistable behavior in HIV tissue sanctuaries.

Combination Antiretroviral Therapy (cART) consists of a cocktail of drugs administered to HIV-infected patients that can suppress the amount of HIV in the patient's blood plasma to an undetectable level. Our previous work has suggested that some HIV-infected patients, despite being placed on cART, can still have ongoing viral replication occurring in self-sustaining inflamed lymph node follicle sanctuary sites. Spatial models of the putative sites show that inflammation is a necessary condition for ongoing HIV replication. In this study, we model the hypothesis that ongoing HIV replication may provide a sufficiently strong pro-inflammatory signal to maintain inflammation levels consistent with continued HIV replication. A system of ordinary differential equations integrated with a reactive-diffusion system is used to model the HIV dynamics and the diameter of a lymph node follicle as a function of time and external influence. The estimates of the parameters in our model come from prior data when available. The results of our study show that these dynamics have two stable steady-state solutions, one with low inflammation and no ongoing HIV replication in the site, and one with high inflammation and high levels of ongoing HIV replication in the site. We furthermore show that the system can transition between the two outcomes in response to a transient exogenous addition of pro-inflammatory signaling, consistent with the antigenic stimulus of a secondary infection. The spatial isolation of the sites results in a low viral load in the blood plasma for both conditions.

Optimal control modulation of HIV reservoir formation rate by antigen infusion.

The Human Immunodeficiency Virus (HIV) infects helper-T cells, and takes advantage of the naturally occurring quiescent phenotype of T cells to persist even under effective treatment conditions. If an infected cell does not produce virus and enters this quiescent state, it forms a natural reservoir that is not targeted by either the existing antiretroviral drugs or the immune system. These quiescent cells intermittently switch to an activated phenotype and begin to produce virus, and are the primary source of viral rebound following treatment cessation. Recent experimental results have shown that, despite this reservoir having a years-long half-life under treatment, most of the cells in the reservoir were infected in a few weeks prior to the start of treatment. This can only be explained by assuming that this reservoir has a short half-life off treatment and a very long half-life on treatment. In this paper, we introduce a novel model of reservoir formation and turnover explaining this difference as a result of antigen-dependent activation. We introduce a second control input through infusion of HIV antigen, mimicking the non-infection pseudovirus (PV) produced by protease inhibitor therapy. This model is coupled to an existing model of immune response to HIV. We fit the parameters of this model to the existing clinical observations of latency. We show that the use of antigen infusion therapy can result in order-of-magnitude decrease in the size of the quiescent reservoir, and that this may provide a way to rapidly stabilize a post-treatment control state in treated HIV infected individuals.

HIV 2-LTR experiment design optimization.

Clinical trials are necessary in order to develop treatments for diseases; however, they can often be costly, time consuming, and demanding to the patients. This paper summarizes several common methods used for optimal design that can be used to address these issues. In addition, we introduce a novel method for optimizing experiment designs applied to HIV 2-LTR clinical trials. Our method employs Bayesian techniques to optimize the experiment outcome by maximizing the Expected Kullback-Leibler Divergence (EKLD) between the a priori knowledge of system parameters before the experiment and the a posteriori knowledge of the system parameters after the experiment. We show that our method is robust and performs equally well if not better than traditional optimal experiment design techniques.

Experiment Design for Early Molecular Events in HIV Infection.

The recent introduction of integrase inhibitors to the HIV antiviral repertoire permits us to create in vitro experiments that reliably terminate HIV infection at the point of chromosomal integration. This allows us to isolate the dynamics of a single round of infection, without needing to account for the influence of multiple overlapping rounds of infection. By measuring the various nucleic acid concentrations in a population of infected target cells at multiple time points, we can infer the rates of these molecular events with great accuracy, which allows us to compare the rates between target cells with different functional phenotypes. This information will help in understanding the behavior of the various populations of reservoir cells such as active and quiescent T-cells which maintain HIV infection in treated patients. In this paper, we introduce a family of models of the early molecular events in HIV infection, with either linear dynamics or age-structured delays at each step. We introduce an experimental design metric based on the delta AIC (Akaike Information Criteria) between a model fit for simulated data from a matching model vs a mismatched model, which allows us to determine a candidate experiment design's ability to discriminate between models. Using parameters values drawn from experimentally-derived priors corrupted with appropriate measurement noise, we confirm that a proposed sampling schedule at different time points allows us to consistently discriminate between candidate models.

Implications of Measurement Assay Type in Design of HIV Experiments.

Time series measurements of circular viral episome (2-LTR) concentrations enable indirect quantification of persistent low-level Human Immunodeficiency Virus (HIV) replication in patients on Integrase-Inhibitor intensified Combined Antiretroviral Therapy (cART). In order to determine the magnitude of these low level infection events, blood has to be drawn from a patients at a frequency and volume that is strictly regulated by the Institutional Review Board (IRB). Once the blood is drawn, the 2-LTR concentration is determined by quantifying the amount of HIV DNA present in the sample via a PCR (Polymerase Chain Reaction) assay. Real time quantitative Polymerase Chain Reaction (qPCR) is a widely used method of performing PCR; however, a newer droplet digital Polymerase Chain Reaction (ddPCR) method has been shown to provide more accurate quantification of DNA. Using a validated model of HIV viral replication, this paper demonstrates the importance of considering DNA quantification assay type when optimizing experiment design conditions. Experiments are optimized using a Genetic Algorithm (GA) to locate a family of suboptimal sample schedules which yield the highest fitness. Fitness is defined as the expected information gained in the experiment, measured by the Kullback-Leibler Divergence (KLD) between the prior and posterior distributions of the model parameters. We compare the information content of the optimized schedules to uniform schedules as well as two clinical schedules implemented by researchers at UCSF and the University of Melbourne. This work shows that there is a significantly greater gain information in experiments using a ddPCR assay vs. a qPCR assay and that certain experiment design considerations should be taken when using either assay.

Order preservation of expected information content using Unscented Transform approximation of multivariate prior distributions in HIV 2-LTR experiment design.

Numerical computation of the expected information content of a prospective experimental design is computationally expensive, requiring calculating the Kullback-Leibler divergence of the posterior distribution from the prior for simulated data from a large sample of points from the prior distribution. In this work, we investigate whether the Unscented Transform (UT) of the prior distribution can provide an adequate estimate of the expected information content in the context of experiment design for a previously validated HIV-1 2-LTR model. Three different schedules with evenly distributed time points have been used to generate the experimental data along with the incorporation of qPCR noise for the study. The UT shows promise in estimating information content by preserving the optimal ordering of 2-LTR sample collection schedules, when compared to completely stochastic sampling from the underlying multivariate distributions.