2021
DOI: 10.3389/fdgth.2021.635524
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Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial

Abstract: Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumo… Show more

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Cited by 22 publications
(9 citation statements)
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References 42 publications
(54 reference statements)
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“…The drug interaction space can then be described by the coefficients arising from the equation and is represented in a smooth response surface. This correlation was first discovered by neural networks and subsequently, validated in multiple in vitro studies 16 , 25 - 37 and prospective human studies in infectious diseases, oncology, and many other indications 29 , 38 - 47 . This platform does not use synergy predictions, big data on pre-existing drug information, or in silico modeling.…”
Section: Introductionmentioning
confidence: 81%
“…The drug interaction space can then be described by the coefficients arising from the equation and is represented in a smooth response surface. This correlation was first discovered by neural networks and subsequently, validated in multiple in vitro studies 16 , 25 - 37 and prospective human studies in infectious diseases, oncology, and many other indications 29 , 38 - 47 . This platform does not use synergy predictions, big data on pre-existing drug information, or in silico modeling.…”
Section: Introductionmentioning
confidence: 81%
“…Subsequent studies resolved this surface, which can rapidly identify optimal combinations, using a second-order algebraic function, with its coefficients determined through a small number of prospective experiments (Abdulla et al, 2020;Al-Shyoukh et al, 2011;Blasiak et al, 2021;Clemens et al, 2019;Ho, 2020;Ho et al, 2020;Lee et al, 2017;Lim et al, 2020;Mohd Abdul Rashid et al, 2015;Rashid et al, 2018;Silva et al, 2016;Wang et al, 2015;Wong et al, 2008). This correlation has subsequently been verified in prospective, human studies in infectious disease, cancer therapy, transplant medicine, and other indications (Tan BKJ et al, 2021;Blasiak et al, 2020;de Mel et al, 2020;Kee et al, 2019;Pantuck et al, 2018;Shen et al, 2020;Zarrinpar et al, 2016). IDentif.AI does not use pre-existing data for algorithm training, in silico modeling, or synergy prediction.…”
Section: Introductionmentioning
confidence: 91%
“…[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] The second order quadratic relationship was also confirmed via prospective human studies. [31][32][33][34][35][36][37] This platform is mechanism agnostic and does not rely on pre-existing data or in silico modeling. All results from IDentif.AI are based on prospectively obtained experimental data, and pinpointed combinations are optimal within the selected pool of drugs.…”
Section: Introductionmentioning
confidence: 99%