Safety, Tolerability, and Parasite Clearance Kinetics in Controlled Human Malaria Infection after Direct Venous Inoculation of Plasmodium falciparum Sporozoites: A Model for Evaluating New Blood-Stage Antimalarial Drugs.

Plasmodium falciparum sporozoite (PfSPZ) direct venous inoculation (DVI) using cryopreserved, infectious PfSPZ (PfSPZ Challenge [Sanaria, Rockville, Maryland]) is an established controlled human malaria infection model. However, to evaluate new chemical entities with potential blood-stage activity, more detailed data are needed on safety, tolerability, and parasite clearance kinetics for DVI of PfSPZ Challenge with established schizonticidal antimalarial drugs. This open-label, phase Ib study enrolled 16 malaria-naïve healthy adults in two cohorts (eight per cohort). Following DVI of 3,200 PfSPZ (NF54 strain), parasitemia was assessed by quantitative polymerase chain reaction (qPCR) from day 7. The approved antimalarial artemether-lumefantrine was administered at a qPCR-defined target parasitemia of ≥ 5,000 parasites/mL of blood. The intervention was generally well tolerated, with two grade 3 adverse events of neutropenia, and no serious adverse events. All 16 participants developed parasitemia after a mean of 9.7 days (95% CI 9.1-10.4) and a mean parasitemia level of 511 parasites/mL (95% CI 369-709). The median time to reach ≥ 5,000 parasites/mL was 11.5 days (95% CI 10.4-12.4; Kaplan-Meier), at that point the geometric mean (GM) parasitemia was 15,530 parasites/mL (95% CI 10,268-23,488). Artemether-lumefantrine was initiated at a GM of 12.1 days (95% CI 11.5-12.7), and a GM parasitemia of 6,101 parasites/mL (1,587-23,450). Mean parasite clearance time was 1.3 days (95% CI 0.9-2.1) and the mean log10 parasite reduction ratio over 48 hours was 3.6 (95% CI 3.4-3.7). This study supports the safety, tolerability, and feasibility of PfSPZ Challenge by DVI for evaluating the blood-stage activity of candidate antimalarial drugs.

Efficient simulation of clinical target response surfaces.

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant-the link to the doses to be administered is difficult to make-or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time-varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

Seeking an optimal dosing regimen for OZ439/DSM265 combination therapy for treating uncomplicated falciparum malaria.

BACKGROUND: The efficacy of artemisinin-based combination therapies (ACTs), the first-line treatments for uncomplicated falciparum malaria, has been declining in malaria-endemic countries due to the emergence of malaria parasites resistant to these compounds. Novel alternative therapies are needed urgently to prevent the likely surge in morbidity and mortality due to failing ACTs. OBJECTIVES: This study investigates the efficacy of the combination of two novel drugs, OZ439 and DSM265, using a biologically informed within-host mathematical model. METHODS: A within-host model was developed, which accounts for the differential killing of these compounds against different stages of the parasite's life cycle and accommodates the pharmacodynamic interaction between the drugs. Data of healthy volunteers infected with falciparum malaria collected from four trials (three that administered OZ439 and DSM265 alone, and the fourth a combination of OZ439 and DSM265) were analysed. Model parameters were estimated in a hierarchical Bayesian framework. RESULTS: The posterior predictive simulations of our model predicted that 800 mg of OZ439 combined with 450 mg of DSM265, which are within the safe and tolerable dose range, can provide above 90% cure rates 42 days after drug administration. CONCLUSIONS: Our results show that the combination of OZ439 and DSM265 can be a promising alternative to replace ACTs. Our model can be used to inform future Phase 2 and 3 clinical trials of OZ439/DSM265, fast-tracking the deployment of this combination therapy in the regions where ACTs are failing. The dosing regimens that are shown to be efficacious and within safe and tolerable limits are suggested for future investigations.

Defining the Antimalarial Activity of Cipargamin in Healthy Volunteers Experimentally Infected with Blood-Stage Plasmodium falciparum.

The spiroindolone cipargamin, a new antimalarial compound that inhibits Plasmodium ATP4, is currently in clinical development. This study aimed to characterize the antimalarial activity of cipargamin in healthy volunteers experimentally infected with blood-stage Plasmodium falciparum Eight subjects were intravenously inoculated with parasite-infected erythrocytes and received a single oral dose of 10 mg cipargamin 7 days later. Blood samples were collected to monitor the development and clearance of parasitemia and plasma cipargamin concentrations. Parasite regrowth was treated with piperaquine monotherapy to clear asexual parasites, while allowing gametocyte transmissibility to mosquitoes to be investigated. An initial rapid decrease in parasitemia occurred in all participants following cipargamin dosing, with a parasite clearance half-life of 3.99 h. As anticipated from the dose selected, parasite regrowth occurred in all 8 subjects 3 to 8 days after dosing and allowed the pharmacokinetic/pharmacodynamic relationship to be determined. Based on the limited data from the single subtherapeutic dose cohort, a MIC of 11.6 ng/ml and minimum parasiticidal concentration that achieves 90% of maximum effect of 23.5 ng/ml were estimated, and a single 95-mg dose (95% confidence interval [CI], 50 to 270) was predicted to clear 109 parasites/ml. Low gametocyte densities were detected in all subjects following piperaquine treatment, which did not transmit to mosquitoes. Serious adverse liver function changes were observed in three subjects, which led to premature study termination. The antimalarial activity characterized in this study supports the further clinical development of cipargamin as a new treatment for P. falciparum malaria, although the hepatic safety profile of the compound warrants further evaluation. (This study has been registered at ClinicalTrials.gov under identifier NCT02543086.).

Population Pharmacokinetics and Pharmacodynamics of Chloroquine in a Plasmodium vivax Volunteer Infection Study.

Chloroquine has been used for the treatment of malaria for > 70 years; however, chloroquine pharmacokinetic (PK) and pharmacodynamic (PD) profile in Plasmodium vivax malaria is poorly understood. The objective of this study was to describe the PK/PD relationship of chloroquine and its major metabolite, desethylchloroquine, in a P. vivax volunteer infection study. We analyzed data from 24 healthy subjects who were inoculated with blood-stage P. vivax malaria and administered a standard treatment course of chloroquine. The PK of chloroquine and desethylchloroquine was described by a two-compartment model with first-order absorption and elimination. The relationship between plasma and whole blood concentrations of chloroquine and P. vivax parasitemia was characterized by a PK/PD delayed response model, where the equilibration half-lives were 32.7 hours (95% confidence interval (CI) 27.4-40.5) for plasma data and 24.1 hours (95% CI 19.0-32.7) for whole blood data. The estimated parasite multiplication rate was 17 folds per 48 hours (95% CI 14-20) and maximum parasite killing rate by chloroquine was 0.213 hour-1 (95% CI 0.196-0.230), translating to a parasite clearance half-life of 4.5 hours (95% CI 4.1-5.0) and a parasite reduction ratio of 400 every 48 hours (95% CI 320-500). This is the first study that characterized the PK/PD relationship between chloroquine plasma and whole blood concentrations and P. vivax clearance using a semimechanistic population PK/PD modeling. This PK/PD model can be used to optimize dosing scenarios and to identify optimal dosing regimens for chloroquine where resistance to chloroquine is increasing.

Confidence and Prediction Intervals for Pharmacometric Models.

Supporting decision making in drug development is a key purpose of pharmacometric models. Pharmacokinetic models predict exposures under alternative posologies or in different populations. Pharmacodynamic models predict drug effects based on exposure to drug, disease, or other patient characteristics. Estimation uncertainty is commonly reported for model parameters; however, prediction uncertainty is the key quantity for clinical decision making. This tutorial reviews confidence and prediction intervals with associated calculation methods, encouraging pharmacometricians to report these routinely.

CODEMamb - an observational communication behavior assessment tool for use in ambulatory dementia care.

INTRODUCTION: Communication improves well-being and quality of life for both people with dementia and their professional and family caregivers. Individualized communication, as required in informed consent procedures and psychosocial interventions, can improve quality of life, especially in ambulatory settings. However, few valid and reliable instruments exist that enable communication to be assessed and communication and behavioral resources to be identified. We, therefore, extended and adapted the newly developed observational instrument CODEM for use in ambulatory settings (CODEMamb). METHODS AND RESULTS: Reliability and validity of the new instrument were studied in a total of 171 patients, whereby principal component analysis revealed three important factors: relationship aspects, verbal communication behavior and nonverbal communication behavior. CODEMamb[Formula: see text]s internal consistency, interrater and retest reliability were satisfactory to excellent. Convergent validity indices, as shown by examining correlations with similar but not identical constructs (CERAD-NP verbal subscales), were medium-high, while the divergent validity index (constructional praxis) was relatively low. The relationship to peer-rating remained nonsignificant. Criterion validity was investigated in groups of patients in accordance with their cognitive status. As expected, verbal communication abilities deteriorate faster than the relationship aspects of communication as the disease progresses. CONCLUSIONS: In summary, CODEMamb is a reliable and valid instrument that can be used to collect important information with the ultimate aim of supporting communication with people with dementia.

Liquid films on shake flask walls explain increasing maximum oxygen transfer capacities with elevating viscosity.

In biotechnological screening and production, oxygen supply is a crucial parameter. Even though oxygen transfer is well documented for viscous cultivations in stirred tanks, little is known about the gas/liquid oxygen transfer in shake flask cultures that become increasingly viscous during cultivation. Especially the oxygen transfer into the liquid film, adhering on the shake flask wall, has not yet been described for such cultivations. In this study, the oxygen transfer of chemical and microbial model experiments was measured and the suitability of the widely applied film theory of Higbie was studied. With numerical simulations of Fick's law of diffusion, it was demonstrated that Higbie's film theory does not apply for cultivations which occur at viscosities up to 10 mPa s. For the first time, it was experimentally shown that the maximum oxygen transfer capacity OTRmax increases in shake flasks when viscosity is increased from 1 to 10 mPa s, leading to an improved oxygen supply for microorganisms. Additionally, the OTRmax does not significantly undermatch the OTRmax at waterlike viscosities, even at elevated viscosities of up to 80 mPa s. In this range, a shake flask is a somehow self-regulating system with respect to oxygen supply. This is in contrary to stirred tanks, where the oxygen supply is steadily reduced to only 5% at 80 mPa s. Since, the liquid film formation at shake flask walls inherently promotes the oxygen supply at moderate and at elevated viscosities, these results have significant implications for scale-up.

Temporal system-level organization of the switch from glycolytic to gluconeogenic operation in yeast.

The diauxic shift in Saccharomyces cerevisiae is an ideal model to study how eukaryotic cells readjust their metabolism from glycolytic to gluconeogenic operation. In this work, we generated time-resolved physiological data, quantitative metabolome (69 intracellular metabolites) and proteome (72 enzymes) profiles. We found that the diauxic shift is accomplished by three key events that are temporally organized: (i) a reduction in the glycolytic flux and the production of storage compounds before glucose depletion, mediated by downregulation of phosphofructokinase and pyruvate kinase reactions; (ii) upon glucose exhaustion, the reversion of carbon flow through glycolysis and onset of the glyoxylate cycle operation triggered by an increased expression of the enzymes that catalyze the malate synthase and cytosolic citrate synthase reactions; and (iii) in the later stages of the adaptation, the shutting down of the pentose phosphate pathway with a change in NADPH regeneration. Moreover, we identified the transcription factors associated with the observed changes in protein abundances. Taken together, our results represent an important contribution toward a systems-level understanding of how this adaptation is realized.

System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes.

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.

Differentiation and visualization of diverse cellular phenotypic responses in primary high-content screening.

High-throughput screening, based on subcellular imaging, has become a powerful tool in lead discovery. Through the generation of high-quality images, not only the specific target signal can be analyzed but also phenotypic changes of the whole cell are recorded. Yet analysis strategies for the exploration of high-content screening results, in a manner that is independent from predefined control phenotypes, are largely missing. The approach presented here is based on a well-established modeling technique, self-organizing maps (SOMs), which uses multiparametric results to group treatments that create similar morphological effects. This report describes a novel visualization of the SOM clustering by using an image of the cells from each node, with the most representative cell highlighted to deploy the phenotype described by each node. The approach has the potential to identify both expected hits and novel cellular phenotypes. Moreover, different chemotypes, which cause the same phenotypic effects, are identified, thus facilitating "scaffold hopping."

Comparison of multivariate data analysis strategies for high-content screening.

High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z' factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

The Interweaving of Cheminformatics and HTS.

The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.

Thermodynamic calculations for biochemical transport and reaction processes in metabolic networks.

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.

Differential glucose repression in common yeast strains in response to HXK2 deletion.

Under aerobic, high glucose conditions, Saccharomyces cerevisiae exhibits glucose repression and thus a predominantly fermentative metabolism. Here, we show that two commonly used prototrophic representatives of the CEN.PK and S288C strain families respond differently to deletion of the hexokinase 2 (HXK2) - a key player in glucose repression: In CEN.PK, growth rate collapses and derepression occurs on the physiological level, while the S288C descendant FY4 Deltahxk2 still grows like the parent strain and shows a fully repressed metabolism. A CEN.PK Deltahxk2 strain with a repaired adenylate cyclase gene CYR1 maintains repression but not growth rate. A comparison of the parent strain's physiology, metabolome, and proteome revealed higher metabolic rates, identical biomass, and byproduct yields, suggesting a lower Snf1 activity and a higher protein kinase A (PKA) activity in CEN.PK. This study highlights the importance of the genetic background in the processes of glucose signaling and regulation, contributes novel evidence on the overlap between the classical glucose repression pathway and the cAMP/PKA signaling pathway, and might have the potential to resolve some of the conflicting findings existing in the field.

Integration of multiple readouts into the z' factor for assay quality assessment.

Methods that monitor the quality of a biological assay (i.e., its ability to discriminate between positive and negative controls) are essential for the development of robust assays. In screening, the most commonly used parameter for monitoring assay quality is the Z' factor, which is based on 1 selected readout. However, biological assays are able to monitor multiple readouts. For example, novel multiparametric screening technologies such as high-content screening provide information-rich data sets with multiple readouts on a compound's effect. Still, assay quality is commonly assessed by the Z' factor based on a single selected readout. This report suggests an extension of the Z' factor, which integrates multiple readouts for assay quality assessment. Using linear projections, multiple readouts are condensed to a single parameter, based on which the assay quality is monitored. The authors illustrate and evaluate this approach using simulated data and real-world data from a high-content screen. The suggested approach is applicable during assay development, to optimize the image analysis, as well as during screening to monitor assay robustness. Furthermore, data sets from high-content imaging assays and other state-of-the-art multiparametric screening technologies, such as flow cytometry or transcript analysis, could be analyzed.

Computational methods to support high-content screening: from compound selection and data analysis to postulating target hypotheses.

BACKGROUND: Computational support for high-content screening (HCS) is of paramount importance at several stages of the process: from the selection of compounds, to the image and data analysis all the way to hit identification and analysis of mechanisms of action. METHOD: Here, we describe computational approaches to improve the benefit gained from HCS, such as compound selection, image analysis and algorithms to further process and explore HCS data. We describe the current challenges in these areas and state our expectations for the field. CONCLUSION: At present there are no standard approaches for correction, normalization, analysis or visualization of HCS data. Thus, the information-rich data sets provided by HCS are exploited to only a limited extent. To overcome this shortcoming, a thorough comparison and evaluation of different tools is needed.

anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data.

BACKGROUND: Compared to other omics techniques, quantitative metabolomics is still at its infancy. Complex sample preparation and analytical procedures render exact quantification extremely difficult. Furthermore, not only the actual measurement but also the subsequent interpretation of quantitative metabolome data to obtain mechanistic insights is still lacking behind the current expectations. Recently, the method of network-embedded thermodynamic (NET) analysis was introduced to address some of these open issues. Building upon principles of thermodynamics, this method allows for a quality check of measured metabolite concentrations and enables to spot metabolic reactions where active regulation potentially controls metabolic flux. So far, however, widespread application of NET analysis in metabolomics labs was hindered by the absence of suitable software. RESULTS: We have developed in Matlab a generalized software called 'anNET' that affords a user-friendly implementation of the NET analysis algorithm. anNET supports the analysis of any metabolic network for which a stoichiometric model can be compiled. The model size can span from a single reaction to a complete genome-wide network reconstruction including compartments. anNET can (i) test quantitative data sets for thermodynamic consistency, (ii) predict metabolite concentrations beyond the actually measured data, (iii) identify putative sites of active regulation in the metabolic reaction network, and (iv) help in localizing errors in data sets that were found to be thermodynamically infeasible. We demonstrate the application of anNET with three published Escherichia coli metabolome data sets. CONCLUSION: Our user-friendly and generalized implementation of the NET analysis method in the software anNET allows users to rapidly integrate quantitative metabolome data obtained from virtually any organism. We envision that use of anNET in labs working on quantitative metabolomics will provide the systems biology and metabolic engineering communities with a mean to proof the quality of metabolome data sets and with all further benefits of the NET analysis approach.

Systematic assignment of thermodynamic constraints in metabolic network models.

BACKGROUND: The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. RESULTS: We present an algorithm that - based on thermodynamics, network topology and heuristic rules - automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to disable thermodynamically infeasible energy production. CONCLUSION: Although not being fully comprehensive, our algorithm for systematic reaction direction assignment could define a significant number of irreversible reactions automatically with low computational effort. We envision that the presented algorithm is a valuable part of a computational framework that assists the automated reconstruction of genome-scale metabolic models.

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data.

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.