News Article | December 8, 2016
LANCASTER, Calif.--(BUSINESS WIRE)--Simulations Plus, Inc. (NASDAQ: SLP), a leading provider of consulting services and software for pharmaceutical discovery and development, today released preliminary revenues for the first quarter of its fiscal year 2017, ending November 30, 2016 (1QFY17). Mr. John Kneisel, chief financial officer of Simulations Plus, stated: “In accordance with our policy to release timely financial information to our shareholders, we are releasing preliminary revenues for 1QFY17. Earnings will not be released until just prior to the filing of our quarterly report on Form 10-Q. We expect to file our 10-Q with the U.S. Securities and Exchange Commission on or before the January 9, 2017 deadline.” John DiBella, vice president for marketing and sales of Simulations Plus, said: “With more pharmaceutical companies requesting the services of our expert consultants on a wide range of projects, plus consistent growth seen with in-house licensing of our technology, FY17 is off to a strong start. We were very pleased to see the U.S. Environmental Protection Agency (EPA) and Centers for Disease Control and Prevention (CDC) continue to add more licenses of our top-ranked ADMET Predictor™ and GastroPlus™ modeling software to assist with their safety research and toxicology initiatives. With training workshops scheduled around the world in 2017, plus new software releases expected over the next few months, we expect to educate more scientists and continue to expand our client base.” About Simulations Plus, Inc. Simulations Plus, Inc. is a premier developer of drug discovery and development software as well as a leading provider of both preclinical and clinical pharmacometric consulting services for regulatory submissions. The company is a global leader focused on improving the ways scientists use knowledge and data to predict the properties and outcomes of pharmaceutical and biotechnology agents. Our software is licensed to and used in the conduct of drug research by major pharmaceutical and biotechnology companies and regulatory agencies worldwide. Our innovations in integrating new and existing science in medicinal chemistry, computational chemistry, pharmaceutical science, biology, and physiology into our software have made us the leading software provider for physiologically based pharmacokinetic modeling and simulation. For more information, visit our website at www.simulations-plus.com. Safe Harbor Statement Under the Private Securities Litigation Reform Act of 1995 – With the exception of historical information, the matters discussed in this press release are forward-looking statements that involve a number of risks and uncertainties. Words like “believe,” “expect” and “anticipate” mean that these are our best estimates as of this writing, but that there can be no assurances that expected or anticipated results or events will actually take place, so our actual future results could differ significantly from those statements. Factors that could cause or contribute to such differences include, but are not limited to: our ability to maintain our competitive advantages, acceptance of new software and improved versions of our existing software by our customers, the general economics of the pharmaceutical industry, our ability to finance growth, our ability to continue to attract and retain highly qualified technical staff, our ability to identify and close acquisitions on terms favorable to the Company, and a sustainable market. Further information on our risk factors is contained in our quarterly and annual reports as filed with the U.S. Securities and Exchange Commission.
Samant T.S.,University of Florida |
Mangal N.,University of Florida |
Lukacova V.,Simulations Plus Inc. |
Schmidt S.,University of Florida
Journal of Clinical Pharmacology | Year: 2015
The establishment of drug dosing in children is often hindered by the lack of actual pediatric efficacy and safety data. To overcome this limitation, scaling approaches are frequently employed to leverage adult clinical information for informing pediatric dosing. The objective of this review is to provide a comprehensive overview of the different scaling approaches used in pediatric pharmacotherapy as well as their proper implementation in drug development and clinical use. We will start out with a brief overview of the current regulatory requirements in pediatric drug development, followed by a review of the most commonly employed scaling approaches in increasing order of complexity ranging from simple body weight-based dosing to physiologically-based pharmacokinetic (PBPK) modeling approaches. Each of the presented approaches has advantages and limitations, which will be highlighted throughout the course of the review by the use of clinically-relevant examples. The choice of the approach employed consequently depends on the clinical question at hand and the availability of sufficient clinical data. The main effort while establishing and qualifying these scaling approaches should be directed towards the development of safe and effective dosing regimens in children rather than identifying the best model, ie models should be fit for purpose. © 2015, The American College of Clinical Pharmacology.
Summary of the National Institute of Child Health and Human Development-Best Pharmaceuticals for Children Act Pediatric Formulation Initiatives Workshop-Pediatric Biopharmaceutics Classification System Working Group
Abdel-Rahman S.M.,Childrens Mercy Hospital |
Amidon G.L.,University of Michigan |
Kaul A.,Cincinnati Childrens Hospital Medical Center |
Kaul A.,University of Cincinnati |
And 4 more authors.
Clinical Therapeutics | Year: 2012
Background: The Biopharmaceutics Classification System (BCS) allows compounds to be classified based on their in vitro solubility and intestinal permeability. The BCS has found widespread use in the pharmaceutical community to be an enabling guide for the rational selection of compounds, formulation for clinical advancement, and generic biowaivers. The Pediatric Biopharmaceutics Classification System (PBCS) Working Group was convened to consider the possibility of developing an analogous pediatric-based classification system. Because there are distinct developmental differences that can alter intestinal contents, volumes, permeability, and potentially biorelevant solubilities at different ages, the PBCS Working Group focused on identifying age-specific issues that need to be considered in establishing a flexible, yet rigorous PBCS. Objective: We summarized the findings of the PBCS Working Group and provided insights into considerations required for the development of a PBCS. Methods: Through several meetings conducted both at The Eunice Kennedy Shriver National Institute of Child Health, Human Development-US Pediatric Formulation Initiative Workshop (November 2011) and via teleconferences, the PBCS Working Group considered several high-level questions that were raised to frame the classification system. In addition, the PBCS Working Group identified a number of knowledge gaps that need to be addressed to develop a rigorous PBCS. Results: It was determined that for a PBCS to be truly meaningful, it needs to be broken down into several different age groups that account for developmental changes in intestinal permeability, luminal contents, and gastrointestinal (GI) transit. Several critical knowledge gaps were identified, including (1) a lack of fully understanding the ontogeny of drug metabolizing enzymes and transporters along the GI tract, in the liver, and in the kidney; (2) an incomplete understanding of age-based changes in the GI, liver, and kidney physiology; (3) a clear need to better understand age-based intestinal permeability and fraction absorbed required to develop the PBCS; (4) a clear need for the development and organization of pediatric tissue biobanks to serve as a source for ontogenic research; and (5) a lack of literature published in age-based pediatric pharmacokinetics to build physiologically- and population-based pharmacokinetic (PBPK) databases. Conclusions: To begin the process of establishing a PBPK model, 10 pediatric therapeutic agents were selected (based on their adult BCS classifications). These agents should be targeted for additional research in the future. The PBCS Working Group also identified several areas where greater emphasis on research was needed to enable the development of a PBCS. © 2012 Elsevier HS Journals, Inc.
De Zordi N.,University of Trieste |
Moneghini M.,University of Trieste |
Kikic I.,University of Trieste |
Grassi M.,University of Trieste |
And 3 more authors.
European Journal of Pharmaceutics and Biopharmaceutics | Year: 2012
The 'classical' loop diuretic drug Furosemide has been used as a model compound to investigate the possibility of enhancing the dissolution rate of poorly water-soluble drugs using supercritical anti-solvent techniques (SASs). In the present study we report upon the in vitro bioavailability improvement of Furosemide through particle size reduction as well as formation of solid dispersions (SDs) using the hydrophilic polymer Crospovidone. Supercritical carbon dioxide was used as the processing medium for these experiments. In order to successfully design a CO 2 antisolvent process, preliminary studies of Furosemide microparticles generation were conducted using Peng Robinson's Equation of State. These preliminary studies indicated using acetone as a solvent with pressures of 100 and 200 bar and a temperature of 313 K would yield optimum results. These operative conditions were then adopted for the SDs. Micronization by means of SAS at 200 bar resulted in a significant reduction of crystallites, particle size, as well as improved dissolution rate in comparison with untreated drug. Furosemide recrystallized by SAS at 100 bar and using traditional solvent evaporation. Moreover, changes in polymorphic form were observed in the 200 bar samples. The physicochemical characterization of Furosemide:crospovidone SDs (1:1 and 1:2 w/w, respectively) generated by SAS revealed the presence of the drug amorphously dispersed in the 1:2 w/w sample at 100 bar still remaining stable after 6 months. This sample exhibits the best in vitro dissolution performance in the simulated gastric fluid (pH 1.2), in comparison with the same SD obtained by traditional method. No interactions between drug and polymer were observed. These results, together with the presence of the selected carrier, confirm that the use of Supercritical fluids antisolvent technology is a valid mean to increase the dissolution rate of poorly soluble drugs. Theoretical in vivo-in vitro relation was predicted by means of a pharmacokinetics mathematical model. © 2012 Elsevier B.V. All rights reserved.
Fraczkiewicz R.,Simulations Plus Inc. |
Lobell M.,Bayer AG |
Goller A.H.,Bayer AG |
Krenz U.,Bayer AG |
And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2015
In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R2) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R2 = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists. © 2014 American Chemical Society.
Woltosz W.S.,Simulations Plus Inc.
Methods in molecular biology (Clifton, N.J.) | Year: 2012
Absorption takes place when a compound enters an organism, which occurs as soon as the molecules enter the first cellular bilayer(s) in the tissue(s) to which is it exposed. At that point, the compound is no longer part of the environment (which includes the alimentary canal for oral exposure), but has become part of the organism. If absorption is prevented or limited, then toxicological effects are also prevented or limited. Thus, modeling absorption is the first step in simulating/predicting potential toxicological effects. Simulation software used to model absorption of compounds of various types has advanced considerably over the past 15 years. There can be strong interactions between absorption and pharmacokinetics (PK), requiring state-of-the-art simulation computer programs that combine absorption with either compartmental pharmacokinetics (PK) or physiologically based pharmacokinetics (PBPK). Pharmacodynamic (PD) models for therapeutic and adverse effects are also often linked to the absorption and PK simulations, providing PK/PD or PBPK/PD capabilities in a single package. These programs simulate the interactions among a variety of factors including the physicochemical properties of the molecule of interest, the physiologies of the organisms, and in some cases, environmental factors, to produce estimates of the time course of absorption and disposition of both toxic and nontoxic substances, as well as their pharmacodynamic effects.
Woltosz W.S.,Simulations Plus Inc.
Journal of Computer-Aided Molecular Design | Year: 2012
In the early days, airplanes were put together with parts designed for other purposes (bicycles, farm equipment, textiles, automotive equipment, etc.). They were then flown by their brave designers to see if the design would work-often with disastrous results. Today, airplanes, helicopters, missiles, and rockets are designed in computers in a process that involves iterating through enormous numbers of designs before anything is made. Until very recently, novel drug-like molecules were nearly always made first like early airplanes, then tested to see if they were any good (although usually not on the brave scientists who created them!). The resulting extremely high failure rate is legendary. This article describes some of the evolution of computer-based design in the aerospace industry and compares it with the progress made to date in computer-aided drug design. Software development for pharmaceutical research has been largely entrepreneurial, with only relatively limited support from government and industry end-user organizations. The pharmaceutical industry is still about 30 years behind aerospace and other industries in fully recognizing the value of simulation and modeling and funding the development of the tools needed to catch up. © 2011 The Author(s).
Clark R.D.,Simulations Plus Inc. |
Waldman M.,Simulations Plus Inc.
Journal of Computer-Aided Molecular Design | Year: 2012
The computational chemistry and cheminformatics community faces many challenges to advancing the state of the art. We discuss three of those challenges here: accurately estimating the contribution of entropy to ligand binding; reliably estimating the uncertainties in model predictions for new molecules; and being able to effectively curate the ever-expanding literature and commercial databases needed to build new models. © 2011 The Author(s).
Clark R.D.,Simulations Plus Inc. |
Liang W.,Simulations Plus Inc. |
Lee A.C.,Simulations Plus Inc. |
Lawless M.S.,Simulations Plus Inc. |
And 2 more authors.
Journal of Cheminformatics | Year: 2014
Background: Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Results: Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Conclusions: Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set. © 2014 Clark et al.; licensee Chemistry Central Ltd.
Clark R.D.,Simulations Plus Inc.
Journal of Medicinal Chemistry | Year: 2013
Developing a viable new drug candidate is difficult. Developing one that is a small molecule kinase inhibitor that binds competitively with respect to ATP with superb selectivity is even more difficult, which makes the design and optimization work described by Jimenez et al. (J. Med. Chem., DOI: 10.1021/jm301465a) particularly remarkable. They took a lead from a high-throughput screen against protein kinase C θ (PKCθ) through a series of optimization steps, culminating in the demonstration of in vivo activity in mice. Having identified and improved the hinge-binding "warhead" at one end of their lead molecule, they proceeded to use structure-based design tools to guide modification of the other end to enhance selectivity over a closely related isoform of the kinase. With that accomplished, they used a series of protection and deprotection maneuvers to modify the central portion of the series scaffold to further enhance potency against the target while also improving pharmacokinetic properties. The project was a success at the preclinical level: oral administration of the ultimate analogue obtained was effective at suppressing interleukin-2 induction in mice. © 2013 American Chemical Society.