FV-429 induces apoptosis by regulating nuclear translocation of PKM2 in pancreatic cancer cells.

Of all malignancies, pancreatic ductal adenocarcinoma (PDAC), constituting 90% of pancreatic cancers, has the worst prognosis. Glycolysis is overactive in PDAC patients and is associated with poor prognosis. Drugs that inhibit glycolysis as well as induce cell death need to be identified. However, glycolysis inhibitors often fail to induce cell death. We here found that FV-429, a derivative of the natural flavonoid wogonin, can induce mitochondrial apoptosis and inhibit glycolysis in PDAC in vivo and in vitro. In vitro, FV-429 inhibited intracellular ATP content, glucose uptake, and lactate generation, consequently leading to mitochondrial dysfunction and apoptosis in PDAC cells. Furthermore, it decreased the expression of PKM2 (a specific form of pyruvate kinase) through the ERK signaling pathway and enhanced PKM2 nuclear translocation. TEPP-46, the activator of PKM2, reversed FV-429-induced glycolysis inhibition and mitochondrial apoptosis in the PDAC cells. In addition, FV-429 exhibited significant tumor suppressor activity and high safety in BxPC-3 cell xenotransplantation models. These results thus demonstrated that FV-429 decreases PKM2 expression through the ERK signaling pathway and enhances PKM2 nuclear translocation, thereby resulting in glycolysis inhibition and mitochondrial apoptosis in PDAC in vitro and in vivo, which makes FV-429 a promising candidate for pancreatic cancer treatment.

GL-V9 inhibits the activation of AR-AKT-HK2 signaling networks and induces prostate cancer cell apoptosis through mitochondria-mediated mechanism.

Prostate cancer (PCa) is a serious health concern for men due to its high incidence and mortality rate. The first therapy typically adopted is androgen deprivation therapy (ADT). However, patient response to ADT varies, and 20-30% of PCa cases develop into castration-resistant prostate cancer (CRPC). This article investigates the anti-PCa effect of a drug candidate named GL-V9 and highlights the significant mechanism involving the AKT-hexokinase II (HKII) pathway. In both androgen receptor (AR)-expressing 22RV1 cells and AR-negative PC3 cells, GL-V9 suppressed phosphorylated AKT and mitochondrial location of HKII. This led to glycolytic inhibition and mitochondrial pathway-mediated apoptosis. Additionally, GL-V9 inhibited AR activity in 22RV1 cells and disrupted the feedback activation of AKT signaling in condition of AR inhibition. This disruption greatly increased the anti-PCa efficacy of the AR antagonist bicalutamide. In conclusion, we present a novel anti-PCa candidate and combination drug strategies to combat CRPC by intervening in the AR-AKT-HKII signaling network.

Converting Nanoflower-like Layered Double Hydroxides into Solvent-Free Nanofluids for CO2 Capture.

Due to the flexibility and versatility of the layered crystal structure of layered double hydroxides (LDHs), they have shown great potential in various fields. However, LDH nanosheets (LDH-NSs) are easy to agglomerate, leading to the problem of accumulation, which hinders their further application. Accordingly, once LDHs are combined with solvent-free nanofluids (SFNs), the advantages of LDHs and SFNs could be combined to achieve an extraordinary performance. However, the stacked structure of traditional LDHs is not conducive to the exposure of hydroxyl functional groups, and hydroxyl sites are key to the conversion of LDHs to SFNs. Therefore, in this work, nanoflower-like LDHs (NFLs) with abundant exposed hydroxyl groups were prepared and combined with organic oligomers to achieve a solid-to-liquid transition. The formation mechanism of NFLs and the grafting mechanism of OS-PEA on their surface were identified. The prepared NFL-F3 still has good fluidity and dispersion stability in different solvents after storage for 100 days. The high-saturated grafting density on the surface of NFLs increased the steric hindrance effect of the nanoparticles, thereby improving the dispersion stability and reducing the viscosity of NFL-F3. Notably, the CO2 sorption performance of NFL-F3 is significantly improved, which is attributed to the voids between polymers, physical sorption, and good fluidity caused by high-saturation grafting on the surface of NFL-F3. Finally, by combining the sorption behavior and model fitting, it was confirmed that the physical effect was dominant in CO2 sorption by the NFL-F, which saved energy for the sorption-desorption process of its industrial application. Moreover, NFL-F3 has a good CO2/N2 separation performance and cycle stability. We envision that this general strategy will open up new insights into the construction of innovative low-viscosity LDH-based SFNs with high CO2 capacity and facilitate CO2/N2 selectivity and offer new directions for LDH utilizations.

Apoptosis induction in human prostate cancer cells related to the fatty acid metabolism by wogonin-mediated regulation of the AKT-SREBP1-FASN signaling network.

Prostate cancer (PCa) cells exploit cellular metabolic reprogramming as their survival advantage, especially aberrant lipid signaling and metabolism. Although recent studies deemed that PCa tends to rely on lipid fuel in comparison with aerobic glycolysis, the relationship between lipid metabolism and cancer growth remains unknown. We demonstrated that wogonin, a naturally occurring mono-flavonoid, could induce apoptosis of PCa cells in vivo and in vitro. Mechanistically, 100 µM wogonin significantly increased the expression of proteins related to the fatty acid synthesis and accumulation as a result of stimulation of AKT phosphorylation and nuclear accumulation of sterol regulatory element-binding protein 1 (SREBP1). The wogonin-induced up-regulation of fatty acid synthase (FASN) promoted fatty acid synthesis and storage, while increased oxidation in mitochondria driven by carnitine palmitoyl-transferase 1A (CPT1A) resulted in the loss of mitochondrial membrane potential and reactive oxygen species (ROS) accumulation, ultimately inducing apoptosis in DU145 and 22Rv1 cells. In vivo, 100 mg/kg of wogonin (i.v.) significantly repressed tumor growth without any obvious toxicity in the PCa xenograft model. In short, we proved that wogonin regulated the fatty acid metabolism and induced apoptosis by activating the AKT-SREBP1-FASN signaling network in human PCa cells, and it exhibited potent anti-tumor effects both in vivo and vitro. Thus it might be a promising candidate for the development of anti-cancer drugs.

Synergistic Regulation of the Microstructure for Multifunctional Graphene Aerogels by a Dual Template Method.

The performance of graphene aerogels (GAs) is based on the microstructure. However, GAs face a challenge of simultaneously controlling the size and alignment of pores strategically. Herein, we initially proposed a simple strategy to construct GAs with an adjustable structure based on the emulsion and ice dual template methods. Specifically, GAs with a honeycomb structure prepared by conventional freezing (CGAs) exhibited a high specific surface of 176 m2/g, superelasticity with a compressive strain of 95%, isotropic compression and thermal insulation performances, as well as an excellent absorption capacity of 150-550 g/g. Instead, the GAs with a bamboo-like network frozen by unidirectional freezing (UGAs) showed anisotropy in compression and thermal insulation behavior. Furthermore, UGAs exhibited incredible special stress (7.9 kPa cm3/mg) along the axial direction twice than that of the radial direction. Meanwhile, the apparent temperature of UGAs was only 45.6 °C when placed on a 120 °C hot stage along the radial direction. Remarkably, the properties of CGAs and UGAs were significantly improved with the adjustment of the microstructure.

Synergetic Photo-Thermo Catalytic Hydrogen Production by Carbon Materials.

Photo-thermo catalytic hydrogen production represents one of the most promising routes for channeling solar energy but typically suffers from high reaction temperatures. In this work, we develop photo-thermo catalytic hydrogen production at low temperatures by cost-effective, nonplasmonic, and metal-free nitrogen-doped carbon materials (CNO1-x). We demonstrate that due to the photothermal conversion of CNO1-x, carrier generation is improved and electron migration is enhanced to suppress the recombination of electron-hole pairs, both of which promote hydrogen production by photocatalysis, while generated hydrogen radicals facilitate the regeneration of active sites for hydrogen production by thermocatalysis. Such synergy greatly promotes photo-thermo catalytic hydrogen production at low temperatures. These results demonstrate the great promise of photo-thermo catalytic hydrogen production over carbon materials at low temperatures.

Mechanically Strong, Thermally Stable Gas Barrier Polyimide Membranes Derived from Carbon Nanotube-Based Nanofluids.

Gas barrier membranes with impressive moisture permeability are highly demanded in air or nature gas dehumidification. We report a novel approach using polyetheramine oligomers covalently grafted on the carbon nanotubes (CNTs) to engineer liquid-like CNT nanofluids (CNT NFs), which are incorporated into a polyimide matrix to enhance the gas barrier and moisture permeation properties. Benefiting from the featured liquid-like characteristic of CNT NFs, a strong interfacial compatibility between CNTs and the polyimide matrix is achieved, and thus, the resulting membranes exhibit high heat resistance and desirable mechanical strength as well as remarkable fracture toughness, beneficially to withstanding creep, impact, and stress fatigue in separation applications. Positron annihilation lifetime spectroscopy measurements indicate a significant decrease in fractional free volume within the resulting membranes, leading to greatly enhanced gas barrier properties while almost showing full retention of moisture permeability compared to that of the pristine membrane. For membranes with 10 wt % CNT NFs, the gas transmission rates, respectively, decrease 99.9% for CH4, 94.4% for CO2, 99.2% for N2, and 97.9% for O2 compared with that of the pristine membrane. Most importantly, with the increasing amount of CNT NFs, the hybrid membranes demonstrate a simultaneous increase of barrier performance and permselectivity for H2O/CH4, H2O/N2, H2O/CO2, and H2O/O2. All these results make these membranes potential candidates for high-pressure natural gas or hyperthermal air dehydration.

2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings.

OBJECTIVES: With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. METHODS: Twelve sets of random forest models were trained using very large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral load in response to a new regimen (absolute models) following virological failure. Two 'standard' models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. RESULTS: The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78-0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral load with a mean absolute error of 0.65 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65-0.75 log10 copies HIV RNA/mL. All models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. All models were significantly better predictors of treatment response than genotyping with rules-based interpretation. CONCLUSIONS: These latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.

A Novel Strategy to Enhance the Performance of CO2 Adsorption Separation: Grafting Hyper-cross-linked Polyimide onto Composites of UiO-66-NH2 and GO.

Graphene oxide (GO) is widely used to improve the pore structure, dispersion capacity, adsorption selectivity, resistance to acids and bases, and thermal stability of metal-organic frameworks (MOFs). However, it remains a daunting challenge to enhance selectivity simply by modifying the pore surface polarity and producing a suitable pore structure for CO2 molecules through a combination of GO with MOFs. Herein, we demonstrate a novel porous hyper-cross-linked polyimide-UiO-graphene composite adsorbent for CO2 capture via in situ chemical knitting and condensation reactions. Specifically, a network of polyimides rich in carbonyl and nitrogen atoms with amino terminations was synthesized via the reaction of 4,4'-oxydiphthalic anhydride (ODPA) and 2,4,6-trimethyl-1,3-phenylenediamine (DAM). The product plays a crucial role in the separation of CO2 from N2. As expected, the resulting composite (PI-UiO/GO-1) exhibited a 3-fold higher CO2 capacity (8.24 vs 2.8 mmol·g-1 at 298 K and 30 bar), 4.2 times higher CO2/N2 selectivity (64.71 vs 15.43), and significantly improved acid-base resistance stability compared with those values of pristine UiO-66-NH2. Furthermore, breakthrough experiments verified that the porous composites can effectively separate CO2 from simulated fuel gas (CO2/N2 = 15/85 vol %) with great potential in industrial applications. More importantly, this strategy can be extended to prepare other MOF-based composites. This clearly advances the development of MOF-polymer materials for gas capture.

An In Situ Coupling Strategy toward Porous Carbon Liquid with Permanent Porosity.

An in situ coupling approach is used to fabricate the porous carbon liquid with permanent porosity by directly dispersing hollow carbon nanospheres in polymerized ionic liquids. It is a kind of homogenous and stable type III porous liquid at room temperature. Because of the well-preserved permanent porosity, this unique porous carbon liquid is capable of absorbing the largest quantity of carbon dioxide than the reference PILs and solid carbon liquid, thus, can function as a promising candidate for application in gas storage. More importantly, this approach not only provides an easy method to tune the properties of those specific porous liquids, but also is suitable for fabricating other porous liquid based on varied porous structures (e.g., porous carbon nitride, porous boron nitride, and polymer with intrinsic microporosity), thus paving a viable path for the rational design and synthesis of novel porous liquids with functional properties for specific applications.

Transforming Metal-Organic Frameworks into Porous Liquids via a Covalent Linkage Strategy for CO2 Capture.

Porous liquids (PLs), an emerging kind of liquid materials with permanent porosity, have attracted increasing attention in gas capture. However, directly turning metal-organic frameworks (MOFs) into PLs via a covalent linkage surface engineering strategy has not been reported. Additionally, challenges including reducing the cost and simplifying the preparation process are daunting. Herein, we proposed a general method to transform Universitetet i Oslo (UiO)-66-OH MOFs into PLs by surface engineering with organosilane (OS) and oligomer species via covalent bonding linkage. The oligomer species endow UiO-66-OH with superior fluidity at room temperature. Meanwhile, the resulting PLs showed great potential in both CO2 adsorption and CO2/N2 selective separation. The residual porosity of PLs was verified by diverse characterizations and molecular simulations. Besides, CO2 selective capture sites were determined by grand canonical Monte Carlo (GCMC) simulation. Furthermore, the universality of the covalent linkage surface engineering strategy was confirmed using different classes of oligomer species and another MOF (ZIF-8-bearing amino groups). Notably, this strategy can be extended to construct other PLs by taking advantages of the rich library of oligomer species, thus making PLs promising candidates for further applications in energy and environment-related fields, such as gas capture, separation, and catalysis.

First detection and genetic characterization of a novel kirkovirus from a dead thoroughbred mare in northern Xinjiang, China, in 2018.

BACKGROUND: In May 2018, a 8 year old thoroughbred mare died at an equestrian club in Changji, Xinjiang, China. The horse had been imported from the United States in 2013. She became pregnant in December 2016 but, after foaling, gradually lost weight and died in May 2018. This study aim to identify the pathogen, who cause of horse death, using virome. RESULTS: We have identified an Equ1-like virus from the fecal virome of a dead thoroughbred mare in China. Full genomic sequencing and phylogenetic analysis of the virus, tentatively named "kirkovirus Cj-7-7", showed that it was closely related to kirkovirus Equ1 and clustered together with po-circo-like viruses 21, 22, 41, and 51, suggesting that it should be assigned to the proposed family "Kirkoviridae". An epidemiological investigation showed that kirkovirus Cj-7-7 circulates in horses of northern Xinjiang and may specifically infect intestinal cells. CONCLUSIONS: Our findings demonstrate the genetic diversity and geographic distribution of Kirkoviruses, and the prevalence of Kirkovirus Cj-7-7 in Xinjiang, China.

Early-stage 11C-Flumazenil PET predicts day-14 selective neuronal loss in a rodent model of transient focal cerebral ischemia.

Predicting tissue outcome early after stroke is an important goal. MRI >3 h accurately predicts infarction but is insensitive to selective neuronal loss (SNL). Previous studies suggest that chronic-stage 11C-flumazenil PET (FMZ-PET) is a validated marker of SNL in rats, while early-stage FMZ-PET may predict infarction. Whether early FMZ-PET also predicts SNL is unknown. Following 45-min distal MCA occlusion, adult rats underwent FMZ-PET at 1 h and 48 h post-reperfusion to map distribution volume (VT), which reflects GABA-A receptor binding. NeuN immunohistochemistry was performed at Day 14. In each rat, VT and %NeuN loss were determined in 44 ROIs spanning the hemisphere. NeuN revealed isolated SNL and cortical infarction in five and one rats, respectively. In the SNL subgroup, VT-1 h was mildly reduced and only weakly predicted SNL, while VT-48 h was significantly increased and predicted SNL both individually (p < 0.01, Kendall) and across the group (p < 0.001), i.e. the higher the VT, the stronger the SNL. Similar correlations were found in the rat with infarction. Our findings suggest GABA-A receptors are still present on injured neurons at the 48 h timepoint, and the increased 48 h VT observed here is consistent with earlier rat studies showing early GABA-A receptor upregulation. That FMZ binding at 48 h was predictive of SNL may have clinical implications.

Role of collateral embolization in addition to uterine artery embolization followed by hysteroscopic curettage for the management of cesarean scar pregnancy.

BACKGROUND: The aim of this study was to assess the feasibility, safety and outcome of the embolization of non-gonadal collateral supplying gestational sac (GS) in addition to uterine artery embolization (UAE), followed by hysteroscopic curettage for the management of cesarean scar pregnancy (CSP). METHODS: A retrospective study was undertaken from January 2012 to September 2018 in 24 CSP patients in whom non-gonadal collaterals supplying GS were identified by arterial angiography performed immediately after UAE. These patients underwent attempt collateral embolization in addition to UAE, followed by hysteroscopic curettage for the management of CSP. The 24 patients were divided into two groups based on whether they underwent technically successful collateral embolization (UAE-SCE group) or failed collateral embolization (UAE-FCE group) in addition to UAE. The baseline characteristics and clinical outcomes including time for serum ß-human chorionic gonadotropin (ß-hCG) levels normalization, blood loss, secondary anemia, and pelvic pain were compared between the two groups. The paired t test and Man Whitney test were used for comparisons of discrete and numerical variables, respectively. RESULTS: Collateral embolization was techinically successful in 16 (66.7%, 16/24) patients and failed in the other 8 (33.3%, 8/24) patients. There were no significant differences between the two groups in baseline characteristics. The mean blood loss and secondary anemia in the UAE-SCE group were significantly less than UAE-FCE group. No significant difference was found between the two groups in the mean time for ß-hCG levels normalization and pelvic pain. CONCLUSIONS: During the management of UAE combined with hysteroscopic curettage for CSP, additional embolization of non-gonadal collateral supplying GS during UAE is feasible and safe in patients with non-gonadal collateral supplying GS, and the additional embolization of the collateral may reduce blood bloss related to hysteroscopic curettage.

Predicting Virological Response to HIV Treatment Over Time: A Tool for Settings With Different Definitions of Virological Response.

OBJECTIVE: Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time. METHODS: Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation. RESULTS: Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67-0.68, mean absolute error of 0.73-0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed. CONCLUSIONS: These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.

Objectives: Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods: Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results: The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. Conclusions: These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.

An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype.

OBJECTIVES: Optimizing antiretroviral drug combination on an individual basis in resource-limited settings is challenging because of the limited availability of drugs and genotypic resistance testing. Here, we describe our latest computational models to predict treatment responses, with or without a genotype, and compare the potential utility of global and local models as a treatment tool for South Africa. METHODS: Global random forest models were trained to predict the probability of virological response to therapy following virological failure using 29 574 treatment change episodes (TCEs) without a genotype, 3179 of which were from South Africa and were used to develop local models. In addition, 15 130 TCEs including genotypes were used to develop another set of models. The 'no-genotype' models were tested with an independent global test set (n = 1700) plus a subset from South Africa (n = 222). The genotype models were tested with 750 independent cases. RESULTS: The global no-genotype models achieved area under the receiver-operating characteristic curve (AUC) values of 0.82 and 0.79 with the global and South African tests sets, respectively, and the South African models achieved AUCs of 0.70 and 0.79. The genotype models achieved an AUC of 0.84. The global no-genotype models identified more alternative, locally available regimens that were predicted to be effective for cases that failed their new regimen in the South African clinics than the local models. Both sets of models were significantly more accurate predictors of outcomes than genotyping with rules-based interpretation. CONCLUSIONS: These latest global models predict treatment responses accurately even without a genotype, out-performed the local South African models and have the potential to help optimize therapy, particularly in resource-limited settings.

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa.

BACKGROUND: Selecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping. OBJECTIVE: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa. METHODS: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative's (RDI's) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs. RESULTS: The models achieved accuracy (area under the receiver-operator characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic. CONCLUSION: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI's models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype.

Computational Models as Predictors of HIV Treatment Outcomes for the Phidisa Cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype; using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load; details of failing and previous antiretroviral drugs; drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative's (RDI's) models used these data to predict the probability of a viral load 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver-operator characteristic curve) of 0.72 when predicting response to therapy; which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI's models have the potential to optimise treatment selection and reduce virological failure in different patient populations; without the use of a genotype

An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.

OBJECTIVES: The optimal individualized selection of antiretroviral drugs in resource-limited settings is challenging because of the limited availability of drugs and genotyping. Here we describe the development of the latest computational models to predict the response to combination antiretroviral therapy without a genotype, for potential use in such settings. METHODS: Random forest models were trained to predict the probability of a virological response to therapy (<50 copies HIV RNA/mL) following virological failure using the following data from 22,567 treatment-change episodes including 1090 from southern Africa: baseline viral load and CD4 cell count, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. The models were assessed during cross-validation and with an independent global test set of 1000 cases including 100 from southern Africa. The models' accuracy [area under the receiver-operating characteristic curve (AUC)] was evaluated and compared with genotyping using rules-based interpretation systems for those cases with genotypes available. RESULTS: The models achieved AUCs of 0.79-0.84 (mean 0.82) during cross-validation, 0.80 with the global test set and 0.78 with the southern African subset. The AUCs were significantly lower (0.56-0.57) for genotyping. CONCLUSIONS: The models predicted virological response to HIV therapy without a genotype as accurately as previous models that included a genotype. They were accurate for cases from southern Africa and significantly more accurate than genotyping. These models will be accessible via the online treatment support tool HIV-TRePS and have the potential to help optimize antiretroviral therapy in resource-limited settings where genotyping is not generally available.