Winchester, MA, United States
Winchester, MA, United States

Time filter

Source Type

Cierkens K.,Applied BioMath | Plano S.,Applied BioMath | Benedetti L.,Applied BioMath | Benedetti L.,WaterWays | And 3 more authors.
Water Science and Technology | Year: 2012

Application of activated sludge models (ASMs) to full-scale wastewater treatment plants (WWTPs) is still hampered by the problem of model calibration of these over-parameterised models. This either requires expert knowledge or global methods that explore a large parameter space. However, a better balance in structure between the submodels (ASM, hydraulic, aeration, etc.) and improved quality of influent data result in much smaller calibration efforts. In this contribution, a methodology is proposed that links data frequency and model structure to calibration quality and output uncertainty. It is composed of defining the model structure, the input data, an automated calibration, confidence interval computation and uncertainty propagation to the model output. Apart from the last step, the methodology is applied to an existing WWTP using three models differing only in the aeration submodel. A sensitivity analysis was performed on all models, allowing the ranking of the most important parameters to select in the subsequent calibration step. The aeration submodel proved very important to get good NH 4 predictions. Finally, the impact of data frequency was explored. Lowering the frequency resulted in larger deviations of parameter estimates from their default values and larger confidence intervals. Autocorrelation due to high frequency calibration data has an opposite effect on the confidence intervals. The proposed methodology opens doors to facilitate and improve calibration efforts and to design measurement campaigns. © IWA Publishing 2012.


Lansita J.A.,ToxStrategies Inc. | Burke J.M.,Applied BioMath | Apgar J.F.,Applied BioMath | Mounho-Zamora B.,Century Inc.
Pharmaceutical Research | Year: 2015

Antibody drug conjugates (ADCs) are promising therapies currently in development for oncology with unique and challenging regulatory and scientific considerations. While there are currently no regulatory guidelines specific for the nonclinical development of ADCs, there are harmonized international guidelines (e.g., ICHS6(R1), ICHM3(R2), ICHS9) that apply to ADCs and provide a framework for their complex development with issues that apply to both small and large molecules. The regulatory and scientific perspectives on ADCs are evolving due to both the advances in ADC technology and a better understanding of the safety and efficacy of ADCs in clinical development. This paper introduces the key scientific and regulatory aspects of the nonclinical development of ADCs, discusses important regulatory and scientific issues in the nonclinical to clinical dose translation of ADCs, and introduces new concepts in the areas of pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation. © 2015 Springer Science+Business Media New York


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 469.94K | Year: 2016

DESCRIPTION provided by applicant Drug development is a very lengthy and expensive undertaking Failure rate for novel drugs exceeds Therefore successful drugs must cover the costs of these failures As such prescription drug prices have escalated at an alarming rate and show no signs of stopping The need for successful drugs to cover failures also means that pharmaceutical companies primarily devote resources to pursuing drug candidates that have a large enough population to allow the company to earn a return on its investment Thus diseases that affect only a small portion of the populace are not investigated nearly as much as say oncology cardiovascular or immunology Current practice usually involves taking modeling techniques developed for small molecule research and trying to adapt them to biologics However this approach more often than not does not provide the scientist with predictions around feasibility and optimal drug properties resulting in wasted effort pursuing leads that have no chance of making it through clinical trials or to be reimbursed by payors Applied BioMath has developed tools that address high value questions in the middle of the drug development pipeline By coupling quantitative systems pharmacology techniques with high performance computing and sophisticated mathematical algorithms we have proven an ability to predict optimal drug properties years before entering the clinic For the past two years we have been offering our services to pharma and biotechs alike to rave reviews We have also been approached with inquiries to license our software This project will fund the development of our proprietary algorithms and toolsets into a stable standardized software platform that can be automatically validated for GLP for by biologics to develop their internal systems pharmacology models At its heart our toolsets are built on Kronecker Bio an open source biophysical computational engine co developed by one of our Founders while pursuing his PhD in Biological Engineering with the Computer Science and Artificial Intelligence Lab from the Massachusetts Institute of Technology This robust platform is currently in use in its raw form in the pharmaceutical industry but is limited in its adoption due to its lack of usability quality contro and GLP validation This project will focus on the application and presentation layer allowing the underlying computational functionality to be easily accessed utilized and understood so capital requirements are less than a typical software development project Achieving our goal of building this software platform is only the first step What follows is a concerted push into the biologics segment which we are currently seeding through our services offering and gaining a reputation as a firm that delivers high value on time We have completed our second round of fundraising raising a total of $ m between both rounds This grant plus the additional fundraising will ensure that we are able to roll out our tools and assist drug companies in delivering best in class biologics that meet unmet medical need on an accelerated timeline to provide patients with a better quality of life Better faster cheaper drugs truly a win win in PUBLIC HEALTH RELEVANCE To reduce the cost of drugs identify fast failures and accelerate the rollout of best in class biotherapeutics analyses in the middle of the drug development pipeline from LI to early clinical trials based on mechanisms and biophysics is needed There are currently a number of open source tools that provide systems pharmacology models for use in research However a lack of a standardized and automated GLP validation scheme prevents the models from continuing through to development A robust systems pharmacology software platform widely utilized by drug companies and upon which fit for purpose systems pharmacology models can be built will greatly reduce the time required to get a drug to market enable the development of best in class drugs increase safety for patients and significantly reduce the development costs associated with biologics We have developed proprietary mechanistic algorithms that we currently use to analyze assays and drug program data for our customers These algorithms are naturally grouped into certain functions toolsets These tools are applicable depending on what questions are being investigated across most all therapeutics areas While our toolsets InVitro Analyzer Biologic Feasibility Biologic Optimizer Biologic PKPD Biologic Mechanistic Covariants are currently in alpha stage being used to create fit for purpose models for our clients they are being used in industry to fulfill our servces contracts We are looking to create an industry standard platform leveraging our proprietary algorithms and toolsets that will allow drug scientists to create fit for purpose models that can be easily validated for GLP use in IND applications We have sampled the market and estimated a potential target audience of over users worldwide We arrived at this number by reviewing the Top Pharma companies by revenue determining employee count from those companiesandapos annual reports using our Founders knowledge of select large and small pharma and biotechs and its employee count and number of employees that could use our software segmenting the list into groups based on revenue extrapolating on a percent basis each companyandapos s applicable target audience assumed companies outside the Top had one quarter of the applicable audience as the th largest company and then summed these estimates This produced a number of over potential customers For validation we determined that the number of people globally employed in the pharmaceutical industry is www statista com So our target market is less than of total pharma employees and our business model shows market penetration of just over of our target market by or less than of total pharmaceutical employees


PubMed | Applied BioMath, Century Inc. and ToxStrategies Inc.
Type: Journal Article | Journal: Pharmaceutical research | Year: 2015

Antibody drug conjugates (ADCs) are promising therapies currently in development for oncology with unique and challenging regulatory and scientific considerations. While there are currently no regulatory guidelines specific for the nonclinical development of ADCs, there are harmonized international guidelines (e.g., ICHS6(R1), ICHM3(R2), ICHS9) that apply to ADCs and provide a framework for their complex development with issues that apply to both small and large molecules. The regulatory and scientific perspectives on ADCs are evolving due to both the advances in ADC technology and a better understanding of the safety and efficacy of ADCs in clinical development. This paper introduces the key scientific and regulatory aspects of the nonclinical development of ADCs, discusses important regulatory and scientific issues in the nonclinical to clinical dose translation of ADCs, and introduces new concepts in the areas of pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation.


PubMed | Applied BioMath
Type: Journal Article | Journal: Water science and technology : a journal of the International Association on Water Pollution Research | Year: 2012

Application of activated sludge models (ASMs) to full-scale wastewater treatment plants (WWTPs) is still hampered by the problem of model calibration of these over-parameterised models. This either requires expert knowledge or global methods that explore a large parameter space. However, a better balance in structure between the submodels (ASM, hydraulic, aeration, etc.) and improved quality of influent data result in much smaller calibration efforts. In this contribution, a methodology is proposed that links data frequency and model structure to calibration quality and output uncertainty. It is composed of defining the model structure, the input data, an automated calibration, confidence interval computation and uncertainty propagation to the model output. Apart from the last step, the methodology is applied to an existing WWTP using three models differing only in the aeration submodel. A sensitivity analysis was performed on all models, allowing the ranking of the most important parameters to select in the subsequent calibration step. The aeration submodel proved very important to get good NH(4) predictions. Finally, the impact of data frequency was explored. Lowering the frequency resulted in larger deviations of parameter estimates from their default values and larger confidence intervals. Autocorrelation due to high frequency calibration data has an opposite effect on the confidence intervals. The proposed methodology opens doors to facilitate and improve calibration efforts and to design measurement campaigns.


Applied BioMath | Entity website

Applied BioMath is currently seeking a talented and innovative PrincipalScientist or Senior Scientist to join our team inLincoln, MA, (the Cambridge area). The ideal candidate will work closely with Pharma and Biotech teams to develop, simulate, and visualize mathematical models to help drive decisions ...


LINCOLN, Mass., Dec. 12, 2016 /PRNewswire/ -- Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, announced their collaboration with Tusk Therapeutics for quantitative systems pharmacology (QSP) modeling and...


Applied BioMath | Entity website

About Us We help biotech and pharmacuetical companies apply advanced mathematical analysis at critical decision points in their drug invention process. We are a passionate, innovative company that delivers meaningful, tangible results in a timely manner ...


News Article | November 15, 2016
Site: www.prnewswire.com

LINCOLN, Mass., Nov. 15, 2016 /PRNewswire/ -- Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, will present at Drug Development Boot Camp on November 16-17, 2016 at The Harvard Club in Boston, MA.  This intensi...

Loading Applied BioMath collaborators
Loading Applied BioMath collaborators