Dose rationale for the use of meropenem/vaborbactam combination in paediatric patients with Gram-negative bacterial infections.

AIMS: Meropenem/vaborbactam combination is approved in adults by FDA and EMA for complicated urinary tract infections and by EMA also for other Gram-negative infections. We aimed to characterise the pharmacokinetics of both moieties in an ongoing study in children and use a model-based approach to inform adequate dosing regimens in paediatric patients. METHODS: Over 4196 blood samples of meropenem and vaborbactam (n = 414 subjects) in adults, together with 114 blood samples (n = 39) in paediatric patients aged 3 months to 18 years were available for this analysis. Data were analysed using a population with prior information from a pharmacokinetic model in adults to inform parameter estimation in children. Simulations were performed to assess the suitability of different dosing regimens to achieve adequate probability of target attainment (PTA). RESULTS: Meropenem/vaborbactam PK was described with two-compartment models with first-order elimination. Body weight and CLcr were significant covariates on the disposition of both drugs. A maturation function was evaluated to explore changes in clearance in neonates. PTA ≥90% was derived for children aged ≥3 months after 3.5-h IV infusion of 40 mg/kg Q8h of both meropenem and vaborbactam and 2 g/2 g for those ≥50 kg. Extrapolation of disposition parameters suggest that adequate PTA is achieved after a 3.5-h IV infusion of 20 mg/kg for neonates and infants (3 months). CONCLUSIONS: An integrated analysis of adult and paediatric data allowed accurate description of sparsely sampled meropenem/vaborbactam PK in paediatric patients and provided recommendations for the dosing in neonates and infants (3 months).

Psychological and Emotional Impact of COVID-19 Pandemic on People Living with Chronic Disease: HIV and Cancer.

People living with chronic disease (PLWCD) are the frailest category, both for the risk of severe COVID-19 illness and for the impact on the care continuum. Aim of this study was to analyze coping strategies and resilience in people living with HIV (PLWH) compared to people living with oncological diseases (PLWOD) during COVID-19 pandemic. We administrated an anonymous questionnaire, which explored the emotional experience, the demographic factors linked to a COVID-19-related stress syndrome, the patient's perception about the adequacy of clinical undertaking from the hospital and the resilience. We analyzed 324 questionnaires. There were no significant differences in prevalence of psychological distress among the whole cohort; however, PLWOD were calmer, less troubled, and more serene than PLWH. Moreover, PLWH smoked more, ate more, and gained more weight than PLWOD. Most patients didn't feel lonely and continued to take pleasure from their activities. No differences in resilience were found between the groups. In the whole cohort lower levels of resilience were found in patients that were unemployed, with history of psychological disorders and in those who experienced more feelings of anger, anxiety and concern. In our study, patients seemed to preserve their well-being, and to activate adaptive coping during the pandemic.

An exploratory pilot study on the involvement of APOE, HFE, C9ORF72 variants and comorbidities in neurocognitive and physical performance in a group of HIV-infected people.

Cognitive decline of aging is modulated by chronic inflammation and comorbidities. In people with HIV-infection (PWH) it may also be affected by HIV-induced inflammation, lifestyle and long-term effects of antiretroviral therapies (ART). The role of genetics in the susceptibility to HIV-associated neurocognitive disorders (HAND) is not fully understood. Here we explored the possible relations among variants in 3 genes involved in inflammation and neurodegenerative disorders (APOE: ε2/ε3/ε4; HFE: H63D; C9ORF72: hexanucleotide expansions ≥ 9 repeats), cognitive/functional impairment (MiniMental State Examination MMSE, Clock Drawing Test CDT, Short Physical Performance Battery SPPB), comorbidities and HIV-related variables in a cohort of > 50 years old PWH (n = 60) with at least 10 years efficient ART. Patients with diabetes or hypertension showed significantly lower MMSE (p = .031) or SPPB (p = .010) scores, respectively, while no relations between HIV-related variables and cognitive/functional scores were observed. Patients with at least one APOEε3 allele had higher CDT scores (p = .019), APOEε2/ε4 patients showing the lowest scores in all tests. Patients with HFE-H63D variant showed more frequently hypertriglyceridemia (p = .023) and those harboring C9ORF72 expansions > 9 repeats had higher CD4+-cell counts (p = .032) and CD4% (p = .041). Multiple linear regression analysis computed to verify possible associations among cognitive/functional scores and all variables further suggested positive association between higher CDT scores and the presence of at least one APOEε3 allele (2,2; 95% CI [0,03 0,8]; p = .037), independent of other variables, although the model did not reach the statistical significance (p = .14). These data suggest that in PWH on efficient ART cognitive abilities and physical performances may be partly associated with comorbidities and genetic background. However, further analyses are needed to establish whether they could be also dependent and influenced by comorbidities and genetic background.

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

MOTIVATION: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. RESULTS: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. AVAILABILITY AND IMPLEMENTATION: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). CONTACT: Giovanni.DiVeroli@cruk.cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-level model for the investigation of oncoantigen-driven vaccination effect.

BACKGROUND: Cancer stem cell theory suggests that cancers are derived by a population of cells named Cancer Stem Cells (CSCs) that are involved in the growth and in the progression of tumors, and lead to a hierarchical structure characterized by differentiated cell population. This cell heterogeneity affects the choice of cancer therapies, since many current cancer treatments have limited or no impact at all on CSC population, while they reveal a positive effect on the differentiated cell populations. RESULTS: In this paper we investigated the effect of vaccination on a cancer hierarchical structure through a multi-level model representing both population and molecular aspects. The population level is modeled by a system of Ordinary Differential Equations (ODEs) describing the cancer population's dynamics. The molecular level is modeled using the Petri Net (PN) formalism to detail part of the proliferation pathway. Moreover, we propose a new methodology which exploits the temporal behavior derived from the molecular level to parameterize the ODE system modeling populations. Using this multi-level model we studied the ErbB2-driven vaccination effect in breast cancer. CONCLUSIONS: We propose a multi-level model that describes the inter-dependencies between population and genetic levels, and that can be efficiently used to estimate the efficacy of drug and vaccine therapies in cancer models, given the availability of molecular data on the cancer driving force.

Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies.

OBJECTIVE: Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD. METHODS: We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023. RESULTS: Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25). CONCLUSIONS: Alterations of alpha synchrony are present even at MCI stage. SIGNIFICANCE: Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.

A pharmacokinetic-pharmacodynamic model for chemoprotective agents against malaria.

Chemoprophylactics are a vital tool in the fight against malaria. They can be used to protect populations at risk, such as children younger than the age of 5 in areas of seasonal malaria transmission or pregnant women. Currently approved chemoprophylactics all present challenges. There are either concerns about unacceptable adverse effects such as neuropsychiatric sequalae (mefloquine), risks of hemolysis in patients with G6PD deficiency (8-aminoquinolines such as tafenoquine), or cost and daily dosing (atovaquone-proguanil). Therefore, there is a need to develop new chemoprophylactic agents to provide more affordable therapies with better compliance through improving properties such as pharmacokinetics to allow weekly, preferably monthly, dosing. Here we present a pharmacokinetic-pharmacodynamic (PKPD) model constructed using DSM265 (a dihydroorotate dehydrogenase inhibitor with activity against the liver schizonts of malaria, therefore, a prophylaxis candidate). The PKPD model mimics the parasite lifecycle by describing parasite dynamics and drug activity during the liver and blood stages. A major challenge is the estimation of model parameters, as only blood-stage parasites can be observed once they have reached a threshold. By combining qualitative and quantitative knowledge about the parasite from various sources, it has been shown that it is possible to infer information about liver-stage growth and its initial infection level. Furthermore, by integrating clinical data, the killing effect of the drug on liver- and blood-stage parasites can be included in the PKPD model, and a clinical outcome can be predicted. Despite multiple challenges, the presented model has the potential to help translation from preclinical to late development for new chemoprophylactic candidates.

Effects of γ-Irradiation on Mating Behavior of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier, 1790) (Coleoptera: Dryophthoridae).

Red palm weevil (RPW) Rhynchophorus ferrugineus (Olivier 1790) is a highly invasive species originating from Southeast Asia and Melanesia. Over the past 30 years, this alien pest has spread extensively in the Middle East and the Mediterranean basin. Its endophagous larvae feed on various palm species, causing significant damage that leads to the death of palm trees. Controlling RPW infestations is challenging due to their gregarious nature and the lack of detectable early symptoms. Systemic insecticides are effective means of control, but their use in urban areas is prohibited and resistance can develop. Considering alternative options with minimal environmental impact, the Sterile Insect Technique (SIT) has been explored. Previous research has shown that male RPWs irradiated at 80 Gy or higher achieve full sterility. This study aimed to investigate in laboratory conditions whether RPW sterile males (irradiated at 60 and 80 Gy) could compete sexually with non-irradiate males. Laboratory bio-assays under both no-choice and choice conditions assessed sexual performance in terms of number of matings, mating duration and time elapsed until the first mating. The results confirmed that irradiation does not negatively affect the mating performance of sterile males, demonstrating their ability to compete successfully with non-irradiated males in both experimental setups.

Diagnostic Accuracy of the Five-Word Test for Mild Cognitive Impairment Due to Alzheimer's Disease.

New diagnostic methods have been developed for the early diagnosis of Alzheimer's disease (AD) with the primary purpose of intercepting the transition-phase (mild cognitive impairment, MCI) between normal aging and dementia. We aimed to explore whether the five-word test (FWT) and the mini-mental state examination (MMSE) are predictive for the early diagnosis of MCI due to AD (AD-MCI). We computed ROC analyses to evaluate the sensitivity and specificity of MMSE and FWT in predicting abnormal CSF (t-Tau, p-Tau181, Aß1−42) and amyloid-PET biomarkers. AD-MCI patients showed lower MMSE and FWT scores (all p < 0.001) than non-AD-MCI. The best predictor of amyloid plaques' presence at amyloid-PET imaging was the encoding sub-score of the FWT (AUC = 0.84). Both FWT and MMSE had low/moderate accuracy for the detection of pathological CSF Aß42, t-Tau and p-Tau181 values, with higher accuracy for the t-Tau/Aß1−42 ratio. In conclusion, the FWT, as a single-domain cognitive screening test, seems to be prompt and moderately accurate tool for the identification of an underlying AD neuropathological process in patients with MCI, supporting the importance of associating biomarkers evaluation in the work-up of patients with dementing neurodegenerative disorders.

Importance of Stability Analysis When Using Nonlinear Semimechanistic Models to Describe Drug-Induced Hematotoxicity.

Stability analysis, often overlooked in pharmacometrics, is essential to explore dynamical systems. The model developed by Friberg et al.1 to describe drug-induced hematotoxicity is widely used to support decisions across drug development, and parameter values are often identified from observed blood counts. We use stability analysis to study the parametric dependence of stable and unstable solutions of several Friberg-type models and highlight the risks associated with system instability in the context of nonlinear mixed effects modeling. We emphasize the consequences of unstable solutions on prediction performance by demonstrating nonbiological system behaviors in a real case study of drug-induced thrombocytopenia. Ultimately, we provide simple criteria for identifying parameters associated with stable solutions of Friberg-type models. For instance, in the original Friberg model, we find that stability depends only on the parameter that governs the feedback from peripheral cells to progenitors and provide the exact range of values that results in stable solutions.

Quantifying Drug-Induced Bone Marrow Toxicity Using a Novel Haematopoiesis Systems Pharmacology Model.

Haematological toxicity associated with cancer therapeutics is monitored by changes in blood cell count, and their primary effect is on proliferative progenitors in the bone marrow. Using observations in rat bone marrow and blood, we characterize a mathematical model that comprises cell proliferation and differentiation of the full haematopoietic phylogeny, with interacting feedback loops between lineages in homeostasis as well as following carboplatin exposure. We accurately predicted the temporal dynamics of several mature cell types related to carboplatin-induced bone marrow toxicity and identified novel insights into haematopoiesis. Our model confirms a significant degree of plasticity within bone marrow cells, with the number and type of both early progenitors and circulating cells affecting cell balance, via feedback mechanisms, through fate decisions of the multipotent progenitors. We also demonstrated cross-species translation of our predictions to patients, applying the same core model structure and considering differences in drug-dependent and physiology-dependent parameters.

Understanding Hematological Toxicities Using Mathematical Modeling.

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

The ATR Inhibitor AZD6738 Synergizes with Gemcitabine In Vitro and In Vivo to Induce Pancreatic Ductal Adenocarcinoma Regression.

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, and overall survival rates have barely improved over the past five decades. The antimetabolite gemcitabine remains part of the standard of care but shows very limited antitumor efficacy. Ataxia telangiectasia and Rad3-related protein (ATR), the apical kinase of the intra-S-phase DNA damage response, plays a central role in safeguarding cells from replication stress and can therefore limit the efficacy of antimetabolite drug therapies. We investigated the ability of the ATR inhibitor, AZD6738, to prevent the gemcitabine-induced intra-S-phase checkpoint activation and evaluated the antitumor potential of this combination in vitro and in vivo In PDAC cell lines, AZD6738 inhibited gemcitabine-induced Chk1 activation, prevented cell-cycle arrest, and restrained RRM2 accumulation, leading to the strong induction of replication stress markers only with the combination. Moreover, synergistic growth inhibition was identified in a panel of 5 mouse and 7 human PDAC cell lines using both Bliss Independence and Loewe models. In clonogenic assays, the combination abrogated survival at concentrations for which single agents had minor effects. In vivo, AZD6738 in combination with gemcitabine was well tolerated and induced tumor regression in a subcutaneous allograft model of a KrasG12D; Trp53R172H; Pdx-Cre (KPC) mouse cancer cell line, significantly extending survival. Remarkably, the combination also induced regression of a subgroup of KPC autochthonous tumors, which generally do not respond well to conventional chemotherapy. Altogether, our data suggest that AZD6738 in combination with gemcitabine merits evaluation in a clinical trial in patients with PDAC. Mol Cancer Ther; 17(8); 1670-82. ©2018 AACR.

An automated fitting procedure and software for dose-response curves with multiphasic features.

In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.

A versatile mathematical work-flow to explore how Cancer Stem Cell fate influences tumor progression.

BACKGROUND: Nowadays multidisciplinary approaches combining mathematical models with experimental assays are becoming relevant for the study of biological systems. Indeed, in cancer research multidisciplinary approaches are successfully used to understand the crucial aspects implicated in tumor growth. In particular, the Cancer Stem Cell (CSC) biology represents an area particularly suited to be studied through multidisciplinary approaches, and modeling has significantly contributed to pinpoint the crucial aspects implicated in this theory. More generally, to acquire new insights on a biological system it is necessary to have an accurate description of the phenomenon, such that making accurate predictions on its future behaviors becomes more likely. In this context, the identification of the parameters influencing model dynamics can be advantageous to increase model accuracy and to provide hints in designing wet experiments. Different techniques, ranging from statistical methods to analytical studies, have been developed. Their applications depend on case-specific aspects, such as the availability and quality of experimental data, and the dimension of the parameter space. RESULTS: The study of a new model on the CSC-based tumor progression has been the motivation to design a new work-flow that helps to characterize possible system dynamics and to identify those parameters influencing such behaviors. In detail, we extended our recent model on CSC-dynamics creating a new system capable of describing tumor growth during the different stages of cancer progression. Indeed, tumor cells appear to progress through lineage stages like those of normal tissues, being their division auto-regulated by internal feedback mechanisms. These new features have introduced some non-linearities in the model, making it more difficult to be studied by solely analytical techniques. Our new work-flow, based on statistical methods, was used to identify the parameters which influence the tumor growth. The effectiveness of the presented work-flow was firstly verified on two well known models and then applied to investigate our extended CSC model. CONCLUSIONS: We propose a new work-flow to study in a practical and informative way complex systems, allowing an easy identification, interpretation, and visualization of the key model parameters. Our methodology is useful to investigate possible model behaviors and to establish factors driving model dynamics. Analyzing our new CSC model guided by the proposed work-flow, we found that the deregulation of CSC asymmetric proliferation contributes to cancer initiation, in accordance with several experimental evidences. Specifically, model results indicated that the probability of CSC symmetric proliferation is responsible of a switching-like behavior which discriminates between tumorigenesis and unsustainable tumor growth.

A mathematical-biological joint effort to investigate the tumor-initiating ability of Cancer Stem Cells.

The involvement of Cancer Stem Cells (CSCs) in tumor progression and tumor recurrence is one of the most studied subjects in current cancer research. The CSC hypothesis states that cancer cell populations are characterized by a hierarchical structure that affects cancer progression. Due to the complex dynamics involving CSCs and the other cancer cell subpopulations, a robust theory explaining their action has not been established yet. Some indications can be obtained by combining mathematical modeling and experimental data to understand tumor dynamics and to generate new experimental hypotheses. Here, we present a model describing the initial phase of ErbB2(+) mammary cancer progression, which arises from a joint effort combing mathematical modeling and cancer biology. The proposed model represents a new approach to investigate the CSC-driven tumorigenesis and to analyze the relations among crucial events involving cancer cell subpopulations. Using in vivo and in vitro data we tuned the model to reproduce the initial dynamics of cancer growth, and we used its solution to characterize observed cancer progression with respect to mutual CSC and progenitor cell variation. The model was also used to investigate which association occurs among cell phenotypes when specific cell markers are considered. Finally, we found various correlations among model parameters which cannot be directly inferred from the available biological data and these dependencies were used to characterize the dynamics of cancer subpopulations during the initial phase of ErbB2+ mammary cancer progression.