In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients.

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

Population pharmacokinetic-pharmacodynamic analysis of givinostat.

BACKGROUND: Givinostat (ITF2357), an oral, synthetic histone deacetylase inhibitor, significantly improved all histological muscle biopsy parameters in a Phase II study in boys with Duchenne muscular dystrophy (DMD). RESEARCH DESIGN AND METHODS: A population pharmacokinetic (PK) model, including seven clinical studies, was developed to explore the effect of covariates on givinostat PK. The final model was qualified to simulate pediatric dosing recommendations. A PK/pharmacodynamic (PD) model was developed to simulate the link between givinostat plasma concentration and platelet time course in 10-70-kg children following 6 months of givinostat 20-70 mg twice daily. RESULTS: A two-compartment model, with first-order input with lag and first-order elimination from the central compartment, described givinostat PK, demonstrating increasing apparent clearance with increasing body weight. The PK/PD model well-described platelet count time course. Weight-based dosing (arithmetic mean systemic exposure of 554-641 ng·h/mL) produced an average platelet count decrease from baseline of 45% with maximum decrease within 28 days. After 1 week and 6 months, ~1% and ~14-15% of patients, respectively, had a platelet count <75 × 109/L. CONCLUSIONS: Based on these data, givinostat dosing will be body weight adjusted and include monitoring of platelet counts to support efficacy and safety in a Phase III DMD study.

A Dynamic Energy Budget (DEB) based modeling framework to describe tumor-in-host growth inhibition and cachexia onset during anticancer treatment in in vivo xenograft studies.

Cancer anorexia-cachexia syndrome (CACS) is a very severe complication of cancer for which an adequate therapeutic strategy has not yet been defined. Recently, a notable number of new animal models of human CACS has been made available for translational purposes. Under the assumption that tumor-induced adaptations of host metabolism and tumor-host energetic competition play a major role in CACS (together with possible toxicities induced by the anticancer treatment), we developed a new Dynamic Energy Budget (DEB)-based framework, modeling tumor-in-host growth dynamics and cachexia onset in preclinical animal models during anticancer treatments. The tumor-in-host modeling approach was successfully applied on a multitude of in vivo preclinical studies involving different host species, tumor cell lines, type of anticancer agents and experimental settings among which standard xenograft studies. Obtained results strongly suggested the adoption of the tumor-in-host DEB-based approach in the preclinical oncological setting for a joint assessment of drug efficacy and toxicity and for a better design of the experiments. Further applications of the DEB-based approach to the context of poly-targeted combination therapy, anti-cachectic treatments and preclinical to clinical translation are under investigation with extremely encouraging preliminary results.

In Vitro-In Vivo Correlation (IVIVC) Population Modeling for the In Silico Bioequivalence of a Long-Acting Release Formulation of Progesterone.

Health authorities carefully evaluate any change in the batch manufacturing process of a drug before and after regulatory approval. In the absence of an adequate in vitro-in vivo correlation (Level A IVIVC), an in vivo bioequivalence (BE) study is frequently required, increasing the cost and time of drug development. This study focused on developing a Level A IVIVC for progesterone vaginal rings (PVRs), a dosage form designed for the continuous delivery in vivo. The pharmacokinetics (PK) of four batches of rings charged with 125, 375, 750 and 1500 mg of progesterone and characterized by different in vitro release rates were evaluated in two clinical studies. In vivo serum concentrations and in vitro release profiles were used to develop a population IVIVC progesterone ring (P-ring) model through a direct differential-equation-based method and a nonlinear-mixed-effect approach. The in vivo release, Rvivo(t), was predicted from the in vitro profile through a nonlinear relationship. Rvivo(t) was used as the input of a compartmental PK model describing the in vivo serum concentration dynamics of progesterone. The proposed IVIVC P-ring model was able to correctly predict the in vivo concentration-time profiles of progesterone starting from the in vitro PVR release profiles. Its internal and external predictability was carefully evaluated considering the FDA acceptance criteria for IVIVC assessment of extended-release oral drugs. Obtained results justified the use of the in vitro release testing in lieu of clinical studies for the BE assessment of any new PVRs batches. Finally, the possible use of the developed population IVIVC model as a simulator of virtual BE trials was explored through a case study.

Bee Visitation and Fruit Quality in Berries Under Protected Cropping Vary Along the Length of Polytunnels.

Wild and managed bees provide effective crop pollination services worldwide. Protected cropping conditions are thought to alter the ambient environmental conditions in which pollinators forage for flowers, yet few studies have compared conditions at the edges and center of growing tunnels. We measured environmental variables (temperature, relative humidity, wind speed, white light, and UV light) and surveyed activity of the managed honey bee, Apis mellifera L.; wild stingless bee, Tetragonula carbonaria Smith; and wild sweat bee, Homalictus urbanus Smith, along the length of 32 multiple open-ended polyethylene growing tunnels. These were spaced across 12 blocks at two commercial berry farms, in Coffs Harbour, New South Wales and Walkamin, North Queensland, Australia. Berry yield, fresh weight, and other quality metrics were recorded at discrete increments along the length of the tunnels. We found a higher abundance and greater number of flower visits by stingless bees and honey bees at the end of tunnels, and less frequent visits to flowers toward the middle of tunnels. The center of tunnels experienced higher temperatures and reduced wind speed. In raspberry, fruit shape was improved with greater pollinator abundance and was susceptible to higher temperatures. In blueberry, per plant yield and mean berry weight were positively associated with pollinator abundance and were lower at the center of tunnels than at the edge. Fruit quality (crumbliness) in raspberries was improved with a greater number of visits by sweat bees, who were not as susceptible to climatic conditions within tunnels. Understanding bee foraging behavior and changes to yield under protected cropping conditions is critical to inform the appropriate design of polytunnels, aid pollinator management within them, and increase economic gains in commercial berry crops.

A Tumor-in-Host DEB-Based Approach for Modeling Cachexia and Bevacizumab Resistance.

Adequate energy intake and homeostasis are fundamental for the appropriate growth and maintenance of an organism; the presence of a tumor can break this equilibrium. Tumor energy requests can lead to extreme weight loss in animals and cachexia in cancer patients. Angiogenesis inhibitors, acting on tumor vascularization, counteract this tumor-host energy imbalance, with significant results in preclinical models and more limited results in the clinic. Current pharmacokinetic-pharmacodynamic models mainly focus on the antiangiogenic effects on tumor growth but do not provide information about host conditions. A model that can predict energetic conditions that provide significant tumor growth inhibition with acceptable host body weight reduction is therefore needed. We developed a new tumor-in-host dynamic energy budget (DEB)-based model to account for the cytostatic activity of antiangiogenic treatments. Drug effect was implemented as an inhibition of the energy fraction subtracted from the host by the tumor. The model was tested on seven xenograft experiments involving bevacizumab and three different tumor cell lines. The model successfully predicted tumor and host body growth data, providing a quantitative measurement of drug potency and tumor-related cachexia. The inclusion of a hypoxia-triggered resistance mechanism enabled investigation of the decreased efficacy frequently observed with prolonged bevacizumab treatments. In conclusion, the tumor-in-host DEB-based approach has been extended to account for the effect of bevacizumab. The resistance model predicts the response to different administration protocols and, for the first time, the impact of tumor-related cachexia in different cell lines. Finally, the physiologic base of the model strongly suggests its use in translational human research. SIGNIFICANCE: A mathematical model describes tumor growth in animal models, taking into consideration the energy balance involving both the growth of tumor and the physiologic functions of the host.

Histological effects of givinostat in boys with Duchenne muscular dystrophy.

Duchenne Muscular Dystrophy (DMD) is caused by mutations in the dystrophin gene leading to dystrophin deficiency, muscle fiber degeneration and progressive fibrotic replacement of muscles. Givinostat, a histone deacetylase (HDAC) inhibitor, significantly reduced fibrosis and promoted compensatory muscle regeneration in mdx mice. This study was conducted to evaluate whether the beneficial histological effects of Givinostat could be extended to DMD boys. Twenty ambulant DMD boys aged 7 to <11 years on stable corticosteroid treatment were enrolled in the study and treated for ≥12 months with Givinostat. A muscle biopsy was collected at the beginning and at the end of treatment to evaluate the amount of muscle and fibrotic tissue. Histological effects were the primary objectives of the study. Treatment with Givinostat significantly increased the fraction of muscle tissue in the biopsies and reduced the amount of fibrotic tissue. It also substantially reduced tissue necrosis and fatty replacement. Overall the drug was safe and tolerated. Improvement in functional tests was not observed in this study, but the sample size of the study was not sufficient to draw definitive conclusions. This study showed that treatment with Givinostat for more than 1 year significantly counteracted histological disease progression in ambulant DMD boys aged 7 to 10 years.

Modeling of human tumor xenografts and dose rationale in oncology.

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Preclinical studies in the mdx mouse model of duchenne muscular dystrophy with the histone deacetylase inhibitor givinostat.

Previous work has established the existence of dystrophin-nitric oxide (NO) signaling to histone deacetylases (HDACs) that is deregulated in dystrophic muscles. As such, pharmacological interventions that target HDACs (that is, HDAC inhibitors) are of potential therapeutic interest for the treatment of muscular dystrophies. In this study, we explored the effectiveness of long-term treatment with different doses of the HDAC inhibitor givinostat in mdx mice--the mouse model of Duchenne muscular dystrophy (DMD). This study identified an efficacy for recovering functional and histological parameters within a window between 5 and 10 mg/kg/d of givinostat, with evident reduction of the beneficial effects with 1 mg/kg/d dosage. The long-term (3.5 months) exposure of 1.5-month-old mdx mice to optimal concentrations of givinostat promoted the formation of muscles with increased cross-sectional area and reduced fibrotic scars and fatty infiltration, leading to an overall improvement of endurance performance in treadmill tests and increased membrane stability. Interestingly, a reduced inflammatory infiltrate was observed in muscles of mdx mice exposed to 5 and 10 mg/kg/d of givinostat. A parallel pharmacokinetic/pharmacodynamic analysis confirmed the relationship between the effective doses of givinostat and the drug distribution in muscles and blood of treated mice. These findings provide the preclinical basis for an immediate translation of givinostat into clinical studies with DMD patients.

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts.

PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.

A4S: a user-friendly graphical tool for pharmacokinetic and pharmacodynamic (PK/PD) simulation.

Effective communication of PK/PD principles and results in a biomedical research environment remains a significant challenge which can result in lack of buy-in and engagement from scientists outside the modeling and simulation communities. In our view, one of the barriers in this area is a lack of user-friendly tools which allow "non experts" to use PK/PD models without the need to develop technical skills and expertise in advanced mathematical principles and specialist software. The costs of commercial software may also prevent large-scale distribution. One attempt to address this issue internally in our research organizations has resulted in the development of the A4S ("Accelera for Sandwich") software, which is a simple-to-use, menu-drive Matlab-based PK/PD simulator targeted at biomedical researchers with little PK/PD experience.

NMS-P937, an orally available, specific small-molecule polo-like kinase 1 inhibitor with antitumor activity in solid and hematologic malignancies.

Polo-like kinase 1 (PLK1) is a serine/threonine protein kinase considered to be the master player of cell-cycle regulation during mitosis. It is indeed involved in centrosome maturation, bipolar spindle formation, chromosome separation, and cytokinesis. PLK1 is overexpressed in a variety of human tumors and its overexpression often correlates with poor prognosis. Although five different PLKs are described in humans, depletion or inhibition of kinase activity of PLK1 is sufficient to induce cell-cycle arrest and apoptosis in cancer cell lines and in xenograft tumor models. NMS-P937 is a novel, orally available PLK1-specific inhibitor. The compound shows high potency in proliferation assays having low nanomolar activity on a large number of cell lines, both from solid and hematologic tumors. NMS-P937 potently causes a mitotic cell-cycle arrest followed by apoptosis in cancer cell lines and inhibits xenograft tumor growth with clear PLK1-related mechanism of action at well-tolerated doses in mice after oral administration. In addition, NMS-P937 shows potential for combination in clinical settings with approved cytotoxic drugs, causing tumor regression in HT29 human colon adenocarcinoma xenografts upon combination with irinotecan and prolonged survival of animals in a disseminated model of acute myelogenous leukemia in combination with cytarabine. NMS-P937, with its favorable pharmacologic parameters, good oral bioavailability in rodent and nonrodent species, and proven antitumor activity in different preclinical models using a variety of dosing regimens, potentially provides a high degree of flexibility in dosing schedules and warrants investigation in clinical settings.

Identification of 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline derivatives as a new class of orally and selective Polo-like kinase 1 inhibitors.

Polo-like kinase 1 (Plk1) is a fundamental regulator of mitotic progression whose overexpression is often associated with oncogenesis and therefore is recognized as an attractive therapeutic target in the treatment of proliferative diseases. Here we discuss the structure-activity relationship of the 4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline class of compounds that emerged from a high throughput screening (HTS) campaign as potent inhibitors of Plk1 kinase. Furthermore, we describe the discovery of 49, 8-{[2-methoxy-5-(4-methylpiperazin-1-yl)phenyl]amino}-1-methyl-4,5-dihydro-1H-pyrazolo[4,3-h]quinazoline-3-carboxamide, as a highly potent and specific ATP mimetic inhibitor of Plk1 (IC(50) = 0.007 microM) as well as its crystal structure in complex with the methylated Plk1(36-345) construct. Compound 49 was active in cell proliferation against different tumor cell lines with IC(50) values in the submicromolar range and active in vivo in the HCT116 xenograft model where it showed 82% tumor growth inhibition after repeated oral administration.

Modelling the genesis and treatment of cancer: the potential role of physiologically based pharmacodynamics.

Physiologically based modelling of pharmacodynamics/toxicodynamics requires an a priori knowledge on the underlying mechanisms causing toxicity or causing the disease. In the context of cancer, the objective of the expert meeting was to discuss the molecular understanding of the disease, modelling approaches used so far to describe the process, preclinical models of cancer treatment and to evaluate modelling approaches developed based on improved knowledge. Molecular events in cancerogenesis can be detected using 'omics' technology, a tool applied in experimental carcinogenesis, but also for diagnostics and prognosis. The molecular understanding forms the basis for new drugs, for example targeting protein kinases specifically expressed in cancer. At present, empirical preclinical models of tumour growth are in great use as the development of physiological models is cost and resource intensive. Although a major challenge in PKPD modelling in oncology patients is the complexity of the system, based in part on preclinical models, successful models have been constructed describing the mechanism of action and providing a tool to establish levels of biomarker associated with efficacy and assisting in defining biologically effective dose range selection for first dose in man. To follow the concentration in the tumour compartment enables to link kinetics and dynamics. In order to obtain a reliable model of tumour growth dynamics and drug effects, specific aspects of the modelling of the concentration-effect relationship in cancer treatment that need to be accounted for include: the physiological/circadian rhythms of the cell cycle; the treatment with combinations and the need to optimally choose appropriate combinations of the multiple agents to study; and the schedule dependence of the response in the clinical situation.

A phase I dose-escalation study of danusertib (PHA-739358) administered as a 24-hour infusion with and without granulocyte colony-stimulating factor in a 14-day cycle in patients with advanced solid tumors.

PURPOSE: This study was conducted to assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of the i.v. pan-aurora kinase inhibitor PHA-739358, danusertib, in patients with advanced solid tumors. EXPERIMENTAL DESIGN: In part 1, patients received escalating doses of danusertib (24-hour infusion every 14 days) without filgrastim (granulocyte colony-stimulating factor, G-CSF). Febrile neutropenia was the dose-limiting toxicity without G-CSF. Further dose escalation was done in part 2 with G-CSF. Blood samples were collected for danusertib pharmacokinetics and pharmacodynamics. Skin biopsies were collected to assess histone H3 phosphorylation (pH3). RESULTS: Fifty-six patients were treated, 40 in part 1 and 16 in part 2. Febrile neutropenia was the dose-limiting toxicity in part 1 without G-CSF. Most other adverse events were grade 1 to 2, occurring at doses >or=360 mg/m(2) with similar incidence in parts 1 and 2. The maximum tolerated dose without G-CSF is 500 mg/m(2). The recommended phase 2 dose in part 2 with G-CSF is 750 mg/m(2). Danusertib showed dose-proportional pharmacokinetics in parts 1 and 2 with a median half-life of 18 to 26 hours. pH3 modulation in skin biopsies was observed at >or=500 mg/m(2). One patient with refractory small cell lung cancer (1,000 mg/m(2) with G-CSF) had an objective response lasting 23 weeks. One patient with refractory ovarian cancer had 27% tumor regression and 30% CA125 decline. CONCLUSIONS: Danusertib was well tolerated with target inhibition in skin at >or=500 mg/m(2). Preliminary evidence of antitumor activity, including a partial response and several occurrences of prolonged stable disease, was seen across a variety of advanced refractory cancers. Phase II studies are ongoing.

A model-based approach to the in vitro evaluation of anticancer activity.

PURPOSE: The use of in vitro screening tests for characterizing the activity of anticancer agents is a standard practice in oncology research and development. In these studies, human A2780 ovarian carcinoma cells cultured in plates are exposed to different concentrations of the compounds for different periods of time. Their anticancer activity is then quantified in terms of EC(50) comparing the number of metabolically active cells present in the treated and the control arms at specified time points. The major concern of this methodology is the observed dependency of the EC(50) on the experimental design in terms of duration of exposure. This dependency could affect the efficacy ranking of the compounds, causing possible biases especially in the screening phase, when compound selection is the primary purpose of the in vitro analysis. To overcome this problem, the applicability of a modeling approach to these in vitro studies was evaluated. METHODS: The model, consisting of a system of ordinary differential equations, represents the growth of tumor cells using a few identifiable and biologically relevant parameters related to cell proliferation dynamics and drug action. In particular, the potency of the compounds can be measured by a unique and drug-specific parameter that is essentially independent of drug concentration and exposure time. Parameter values were estimated using weighted nonlinear least squares. RESULTS: The model was able to adequately describe the growth of tumor cells at different experimental conditions. The approach was validated both on commercial drugs and discovery candidate compounds. In addition, from this model the relationship between EC(50) and the exposure time was derived in an analytic form. CONCLUSIONS: The proposed approach provides a new tool for predicting and/or simulating cell responses to different treatments with useful indications for optimizing in vitro experimental designs. The estimated potency parameter values obtained from different compounds can be used for an immediate ranking of anticancer activity.

A minimal model of tumor growth inhibition.

The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.

PHA-739358, a potent inhibitor of Aurora kinases with a selective target inhibition profile relevant to cancer.

PHA-739358 is a small-molecule 3-aminopyrazole derivative with strong activity against Aurora kinases and cross-reactivities with some receptor tyrosine kinases relevant for cancer. PHA-739358 inhibits all Aurora kinase family members and shows a dominant Aurora B kinase inhibition-related cellular phenotype and mechanism of action in cells in vitro and in vivo. p53 status-dependent endoreduplication is observed upon treatment of cells with PHA-739358, and phosphorylation of histone H3 in Ser(10) is inhibited. The compound has significant antitumor activity in different xenografts and spontaneous and transgenic animal tumor models and shows a favorable pharmacokinetic and safety profile. In vivo target modulation is observed as assessed by the inhibition of the phosphorylation of histone H3, which has been validated preclinically as a candidate biomarker for the clinical phase. Pharmacokinetics/pharmacodynamics modeling was used to define drug potency and to support the prediction of active clinical doses and schedules. We conclude that PHA-739358, which is currently tested in clinical trials, has great therapeutic potential in anticancer therapy in a wide range of cancers.

Development and validation of in silico models for estimating drug preformulation risk in PEG400/water and Tween80/water systems.

Solubility is one of the most important properties of drug candidates for achieving the targeted plasma concentrations following oral dosing. Furthermore, the formulations adopted in the in vivo preclinical studies, for both oral and intravenous administrations, are usually solutions. To formulate compounds sparingly soluble in water, pharmaceutically acceptable cosolvents or surfactants are typically employed to increase solubility. Compounds poorly soluble also in these systems will likely show severe formulation issues. In such cases, relatively high amount of compounds, rarely available in the early preclinical phases, are needed to identify the most appropriate dosing vehicles. Hence, the purpose of this study was to build two computational models which, on the basis of the molecular structure, are able to predict the compound solubility in two vehicle systems (40% PEG400/water and 10% Tween80/water) used in our company as screening tools for anticipating potential formulation issues. The two models were developed using the solubility data obtained from the analysis of approximately 2000 chemically diverse compounds. The structural diversity and the drug-like space covered by these molecules were investigated using the ChemGPS methodology. The compounds were classified (high/low preformulation risk) based on the experimental solubility value range. A combination of descriptors (i.e. logD at two different pH, E-state indices and other 2D structural descriptors) was correlated to these classes using partial least squares discriminant (PLSD) analysis. The overall accuracy of each PLSD model applied to independent sets of compounds was approximately 78%. The accuracy reached when the models were used in combination to identify molecules with low preformulation risk in both systems was 83%. The models appeared a valuable tool for predicting the preformulation risk of drug candidates and consequently for identifying the most appropriate dosing vehicles to be further investigated before the first in vivo preclinical studies. Since only a small number of 2D descriptors is need to evaluate the preformulation risk classes, the models resulted easy to use and characterized by high throughput.