Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.Comment: To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary materia

Combining Kernel and Model Based Learning for HIV Therapy Selection

We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods

The future of long-acting cabotegravir plus rilpivirine therapy: deeds and misconceptions

HIV infection is currently managed as a chronic disease because of improvements in antiretroviral therapy (ART). Switching to a new regimen is a natural event during long-term therapy to avoid problems related to toxicity, adherence, failure, and potential selection of drug resistance. The development of co-formulations of multiple agents in one pill, and novel drug classes and drugs with a high genetic barrier to resistance have been important in this context. The approval of the long-acting, once-monthly or bimonthly injectable combination of the second-generation strand transfer integrase inhibitor (InSTI), cabotegravir (CAB) together with the non-nucleoside reverse transcriptase inhibitor (NNRTI), rilpivirine (RPV) represents the most recent achievement in the search for potent and convenient ART. Several pivotal trials (such as LATTE-2, ATLAS, FLAIR, and ATLAS-2M) showed the high efficacy and safety of this long-acting formulation used as an induction-maintenance strategy. Few confirmed virological failures (CVF) have been observed. The combination of at least two of the following baseline factors, HIV-1 subtype A6/A1, a body mass index (BMI) ≥30 kg/m2, and RPV resistance-associated mutations, was associated with an increased risk of CVF at week 48. The data indicate that this long-acting therapeutic strategy is attractive and potent; therefore, defining the most appropriate patient for this treatment and how to handle practical issues is warranted

Distribution of different HBV DNA forms in plasma and peripheral blood mononuclear cells (PBMCs) of chronically infected patients with low or undetectable HBV plasma viremia

Few studies have documented hepatitis B virus (HBV) DNA in peripheral blood mononuclear cells (PBMCs). We developed real-time PCR methods for differential amplification of covalently closed circular (cccDNA) and total HBV DNA (tDNA). The different distribution of cccDNA and tDNA in plasma and PBMCs was evaluated in 37 patients with low or undetectable viremia. Plasma tDNA measured by the Abbott reference system and the in-house assay correlated well (Spearman rho = 0.804; P<0.0001). tDNA was detected in four PBMC samples, all from patients with detectable plasma viremia (range 633-6,406 IU/ml), cccDNA was not detected in any sample. The reasons for apparently discrepant results need further investigation but possibly include the high diversification of HBV status and plasma viremia levels

Potential role of doravirine for the treatment of HIV-1-infected persons with transmitted drug resistance

Background: Doravirine has a unique resistance profile but how this profile might increase its usefulness beyond first-line therapy in persons with susceptible viruses has not been well studied. We sought to determine scenarios in which doravirine would retain activity against isolates from ART-naïve persons with transmitted drug resistance (TDR) and to identify gaps in available doravirine susceptibility data. Methods: We analyzed published in vitro doravirine susceptibility data and applied the results to 42,535 RT sequences from ART-naïve persons published between 2017 and 2021. NNRTI drug resistance mutations (DRMs) were defined as those with a Stanford HIV Drug Resistance Database doravirine penalty score either alone or in combination with other mutations. Results: V106A, Y188L, F227C/L, M230L, and Y318F were associated with the greatest reductions in doravirine susceptibility. However, several NNRTI DRMs and DRM combinations lacking these canonical resistance mutations had > tenfold reduced susceptibility including G190E, one isolate with G190S, three isolates with L100I + K103N, one isolate with K103N + P225H, and isolates with L100I + K103N + V108I and K101E + Y181C + G190A. Of the 42,535 ART-naïve sequences, 3,374 (7.9%) contained a NNRTI DRM of which 2,788 (82.6%) contained 1 DRM (n = 33 distinct mutations), 426 (12.6%) contained 2 DRMs (79 distinct pairs of mutations), and 143 (4.2%) contained ≥ 3 DRMs (86 distinct mutation patterns). Among the 2,788 sequences with one DRM, 112 (4.0%) were associated with ≥ 3.0-fold reduced doravirine susceptibility while 2,625 (94.2%) were associated with < 3.0-fold reduced susceptibility. Data were not available for individual NNRTI DRMs in 51 sequences (1.8%). Among the 426 sequences with two NNRTI DRMs, 180 (42.3%) were associated with ≥ 3.0 fold reduced doravirine susceptibility while just 32 (7.5%) had < 3.0 fold reduced susceptibility. Data were not available for 214 (50.2%) sequences containing two NNRTI DRMs. Conclusions: First-line therapy containing doravirine plus two NRTIs is expected to be effective in treating most persons with TDR as more than 80% of TDR sequences had a single NNRTI DRM and as more than 90% with a single DRM were expected to be susceptible to doravirine. However, caution is required for the use of doravirine in persons with more than one NNRTI DRM even if none of the DRMs are canonical doravirine-resistance mutations. © 2023, The Author(s)

Development of a Cell-Based Immunodetection Assay for Simultaneous Screening of Antiviral Compounds Inhibiting Zika and Dengue Virus Replication:

Practical cell-based assays can accelerate anti-Zika (ZIKV) and anti-dengue (DENV) virus drug discovery. We developed an immunodetection assay (IA), using a pan-flaviviral monoclonal antibody recognizing a conserved envelope domain. The final protocol includes a direct virus yield reduction assay (YRA) carried out in the human Huh7 cell line, followed by transfer of the supernatant to a secondary Huh7 culture to characterize late antiviral effects. Sofosbuvir and ribavirin were used to validate the assay, while celgosivir was used to evaluate the ability to discriminate between early and late antiviral activity. In the direct YRA, at 100, 50, and 25 TCID50, sofosbuvir IC50 values were 5.0 ± 1.5, 2.7 ± 0.5, 2.5 ± 1.1 µM against ZIKV and 16.6 ± 2.8, 4.6 ± 1.4, 2.6 ± 2.2 µM against DENV; ribavirin IC50 values were 6.8 ± 4.0, 3.8 ± 0.6, 4.5 ± 1.4 µM against ZIKV and 17.3 ± 4.6, 7.6 ± 1.2, 4.1 ± 2.3 µM against DENV. Sofosbuvir and ribavirin IC50 values determined in the secondary YRA were reproducible and comparable with those obtained by direct YRA and plaque reduction assay (PRA). In agreement with the proposed mechanism of late action, celgosivir was active against DENV only in the secondary YRA (IC50 11.0 ± 1.0 µM) and in PRA (IC50 10.1 ± 1.1 µM). The assay format overcomes relevant limitations of the gold standard PRA, allowing concurrent analysis of candidate antiviral compounds against different viruses and providing preliminary information about early versus late antiviral activity

Stability of unfrozen whole blood DNA for remote genotypic analysis of HIV-1 coreceptor tropism

Maraviroc is an HIV-1 coreceptor antagonist that has shown good efficacy and tolerability in treatment-naive and treatment-experienced patients harboring CCR5-tropic virus. The use of Maraviroc in treatment simplification in patients with suppressed plasma HIV-1 RNA requires analysis of HIV-1 DNA. Coreceptor tropism testing is often performed remotely at reference laboratories. In this study paired whole blood stored at + 4 °C and at-20°C were compared as a source for genotypic coreceptor tropism testing

Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network)

: The EuResist cohort was established in 2006 with the purpose of developing a clinical decision-support tool predicting the most effective antiretroviral therapy (ART) for persons living with HIV (PLWH), based on their clinical and virological data. Further to continuous extensive data collection from several European countries, the EuResist cohort later widened its activity to the more general area of antiretroviral treatment resistance with a focus on virus evolution. The EuResist cohort has retrospectively enrolled PLWH, both treatment-naïve and treatment-experienced, under clinical follow-up from 1998, in nine national cohorts across Europe and beyond, and this article is an overview of its achievement. A clinically oriented treatment-response prediction system was released and made available online in 2008. Clinical and virological data have been collected from more than one hundred thousand PLWH, allowing for a number of studies on the response to treatment, selection and spread of resistance-associated mutations and the circulation of viral subtypes. Drawing from its interdisciplinary vocation, EuResist will continue to investigate clinical response to antiretroviral treatment against HIV and monitor the development and circulation of HIV drug resistance in clinical settings, along with the development of novel drugs and the introduction of new treatment strategies. The support of artificial intelligence in these activities is essential

Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV

Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to mitigate this problem is by generating realistic synthetic datasets using generative adversarial networks (GANs). However, GANs are known to suffer from mode collapse thus creating outputs of low diversity. This lowers the quality of the synthetic healthcare data, and may cause it to omit patients of minority demographics or neglect less common clinical practices. In this paper, we extend the classic GAN setup with an additional variational autoencoder (VAE) and include an external memory to replay latent features observed from the real samples to the GAN generator. Using antiretroviral therapy for human immunodeficiency virus (ART for HIV) as a case study, we show that our extended setup overcomes mode collapse and generates a synthetic dataset that accurately describes severely imbalanced class distributions commonly found in real-world clinical variables. In addition, we demonstrate that our synthetic dataset is associated with a very low patient disclosure risk, and that it retains a high level of utility from the ground truth dataset to support the development of downstream machine learning algorithms.Comment: In the near future, we will make our codes and synthetic datasets publicly available to facilitate future research. Follow us on https://healthgym.ai

Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction

BACKGROUND: Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood. MATERIALS AND METHODS: We developed a method for predicting RC from genotype using support vector machines (SVMs) trained on about 300 genotype-RC pairs. Next, we studied the impact of predicted viral RC (pRC) on the change of viral load (VL) and CD4(+) T-cell count (CD4) during the course of therapy on about 3,000 treatment change episodes (TCEs) extracted from the EuResist integrated database. Specifically, linear regression models using either treatment activity scores (TAS), the drug combination, or pRC or any combination of these covariates were trained to predict change in VL and CD4, respectively. RESULTS: The SVM models achieved a Spearman correlation (rho) of 0.54 between measured RC and pRC. The prediction of change in VL (CD4) was best at 180 (360) days, reaching a correlation of rho = 0.45 (rho = 0.27). In general, pRC was inversely correlated to drug resistance at treatment start (on average rho = -0.38). Inclusion of pRC in the linear regression models significantly improved prediction of virological response to treatment based either on the drug combination or on the TAS (t-test; p-values range from 0.0247 to 4 10(-6)) but not for the model using both TAS and drug combination. For predicting the change in CD4 the improvement derived from inclusion of pRC was not significant. CONCLUSION: Viral RC could be predicted from genotype with moderate accuracy and could slightly improve prediction of virological treatment response. However, the observed improvement could simply be a consequence of the significant correlation between pRC and drug resistance