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"We used QSP modeling to explore the PK of antibody-drug conjugate (ADC) therapeutics which typically show a discrepancy between the PK of total antibody and that of conjugated antibody, carrying one or more payload molecules," said Joshua Apgar, PhD, Co-Founder and CSO at Applied BioMath.  "This discrepancy is often attributed to deconjugation, however recent evidence suggests that the underlying mechanisms may be more complex. Modeling helps generate hypotheses that predict this complex behavior." "This ADC case study is a great example of how Model-Aided Drug Invention (MADI) helps project teams quickly discern important characteristics of a platform or drug candidate," said John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.  "We often hear from our clients how valuable these biological and drug insights, sometimes counterintuitive, are as it helps accelerate drug candidates into the clinic and to then better ensure the likelihood of testing proof of clinical concept." This webinar will air live at 2pm ET / 11am PT and is ideal for scientists interested in how computational models can be used to design novel ADCs and to optimize the discovery and development of existing ADCs.  For more information on this webinar and to register, visit: www.appliedbiomath.com/20170913_webinar Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D.  Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward.  Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug.  We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies. Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.


Applied BioMath's poster highlights how their quantitative systems pharmacology (QSP) approach provides biological insights into the impact of drug-to-antibody ratio and the resulting changes in molecular properties on overall pharmacokinetics (PK) and relative payload disposition as observed in preclinical and clinical studies.  This approach, based on research led by Anna Katharina Wilkins, PhD, Principal Scientist at Applied BioMath, explores the PK of antibody-drug conjugate (ADC) therapeutics which typically show a discrepancy between the PK of total antibody and that of conjugated antibody, carrying one or more payload molecules. This discrepancy is often attributed to deconjugation, however recent evidence suggests that the underlying mechanisms may be more complex. "This antibody-drug conjugate study is a great example of how QSP modeling approaches provide insights, some of which are counterintuitive, into complex therapeutic platforms. These insights help our collaborators accelerate the development of potentially best-in-class therapeutics and quickly get drug candidates into the clinic, with higher likelihoods of testing proof of clinical concept, ultimately to help patients." Applied BioMath is well versed in the use of QSP modeling approaches for ADC therapeutics.  Their inaugural presentation of QSP approaches for ADC research received top honors at World ADC 2016.  They also announced a recent ADC collaboration in PRNewswire.com's article entitled "Applied BioMath, LLC announces collaboration with Zymeworks Inc. for Clinical Trial Support in Oncology." Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D.  Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward.  Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug.  We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies. Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.


Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath, will present two case studies, one highlighting an example of integrating systems modeling to predict optimal drug properties targeting PD-1 and TIM3 in immuno-oncology for bispecific biologics and fixed dose combinations, and a second showing QSP approaches to determine best in class properties for targeted anabolic growth factor to arthritic joints. "Our proprietary QSP modeling approach has a proven track record of helping biotechnology and pharmaceutical companies better understand their drug candidate's mechanism of action in the context of human disease mechanisms. Our model results have impacted optimal lead generation design and better enabled clinical candidate selection and IND submission," said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.  "These case studies are just two of many examples we have where our participation in the project has de-risked projects, accelerated the discovery and development of best-in-class therapeutics, and impacted critical decisions, in the continuum from preclinical exploration to clinical research." Immediately prior to this event, Dr. Burke will present at the 8th Annual World Bispecific Summit in Boston, MA.   Additionally, Dr. Burke will present a variety of case studies at the American Conference of Pharmacometrics (AcoP) October 15-18, 2017 in Ft. Lauderdale, Florida.   For more information on any of these events, please visit www.appliedbiomath.com/events. Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D.   Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward.  Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug.  We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies. Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.


News Article | September 18, 2017
Site: www.prnewswire.com

John Burke, PhD, Co-Founder, President and CEO will present a talk titled "Model Aided Drug Invention Case Studies: Quantitative Modeling and Simulation Approaches Driving Critical Decisions from Research through Clinical Trials." Model Aided Drug Invention (MADI) is a mathematical modeling and engineering approach to translational medicine, which includes mechanistic PKPD and systems pharmacology, that aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms. Dr. Burke's discussion will analyze: "We are excited to participate in the Fierce Drug Development Forum for the first time this fall," said John Burke, PhD, Co-Founder, President and CEO of Applied BioMath. "The MADI case studies in the presentation are just a few of many examples we have where our participation in a project has resulted in helping industry save money, accelerated timelines, and made better therapeutics, ultimately improving patients' lives." Additionally this fall, Applied BioMath is participating in the World Bispecific Summit, a Boston PBSS Minisymposium, and the American Conference on Pharmacometrics (ACoP). For more information about Applied BioMath, please visit www.appliedbiomath.com. Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D.    Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward.   Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug.  We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies. Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.


"Systems Pharmacology is a mathematical modeling and engineering approach to translational medicine that aims to quantitatively integrate knowledge about therapeutics with an understanding of its mechanism of action in the context of human disease mechanisms," said Dr. John Burke, PhD, Co-Founder, President, and CEO of Applied BioMath.  "We are excited to share two of our case studies that highlight examples of our Systems Pharmacology efforts that have de-risked projects, accelerated the discovery and development of best-in-class therapeutics, and impacted critical decisions." Dr. Burke will immediately follow-up his presentation at the World Bispecific Summit with a talk at the Pharmaceutical BioSciences Society (PBSS), entitled: "Model-informed drug discovery and development using Systems Pharmacology modeling: an industry and regulatory perspective", September 29, 2017.   Additionally, you can find Applied BioMath sponsoring and showcasing multiple case studies at the American Conference of Pharmacometrics (AcoP) October 15-18, 2017 in Ft. Lauderdale, Florida. For more information on any of these events, please visit www.appliedbiomath.com/events. Applied BioMath (www.appliedbiomath.com), the industry-leader in applying mechanistic modeling to drug research and development, helps biotechnology and pharmaceutical companies answer complex, critical Go/No-go decisions in R&D.    Applied BioMath leverages biology, proprietary mathematical modeling and analysis technology, high-performance computing, and decades of industry experience to help groups better understand their candidate, its best-in-class parameters, competitive advantages, and the best path forward.   Our involvement shortens project timelines, lowers cost, and increases the likelihood of a best-in-class drug.  We provide clarity to complex situations, answer otherwise unanswerable questions, and our approach, when validated in the clinic, is 10x more accurate than traditional methodologies. Applied BioMath and the Applied BioMath logo are trademarks of Applied BioMath, LLC.


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.


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


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...


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...

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