Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma

Mel, Sanjay de; Rashid, Masturah Bte Mohd Abdul; Zhang, Xi Yun; Goh, Jonathan; Lee, Chun Tsu; Poon, Li Mei; Chan, Esther Hian Li; Liu, Clementine Xin; Chng, Wee Joo; Chee, Yen Lin; Lee, Joanne; Yuen, Yi Ching; Lim, Jing Quan; Chia, Burton K.; Laurensia, Yurike; Huang, Dachuan; Pang, Wan Lu; Cheah, Daryl Ming Zhe; Wong, Esther Kam Yin; Ong, Choon Kiat; Tang, Tiffany; Lim, Soon Thye; Ng, Siok Bian; Tan, Soo Yong; Loi, Hoi-Yin; Tan, Lip Kun; Chow, Edward Kai‐Hua; Jeyasekharan, Anand D.

doi:10.1038/s41408-020-0276-7

Cited by 27 publications

(21 citation statements)

References 15 publications

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“…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%

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

Blasiak

Truong

Remus

et al. 2021

Preprint

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Objectives: We aimed to harness IDentif.AI 2.0, a clinically actionable AI platform to rapidly pinpoint and prioritize optimal combination therapy regimens against COVID-19. Methods: A pool of starting candidate therapies was developed in collaboration with a community of infectious disease clinicians and included EIDD-1931 (metabolite of EIDD-2801), baricitinib, ebselen, selinexor, masitinib, nafamostat mesylate, telaprevir (VX-950), SN-38 (metabolite of irinotecan), imatinib mesylate, remdesivir, lopinavir, and ritonavir. Following the initial drug pool assessment, a focused, 6-drug pool was interrogated at 3 dosing levels per drug representing nearly 10,000 possible combination regimens. IDentif.AI 2.0 paired prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus (propagated, original strain and B.1.351 variant) and Vero E6 assay with a quadratic optimization workflow. Results: Within 3 weeks, IDentif.AI 2.0 realized a list of combination regimens, ranked by efficacy, for clinical go/no-go regimen recommendations. IDentif.AI 2.0 revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived. Conclusions: IDentif.AI 2.0 rapidly revealed promising drug combinations for a clinical translation. It pinpointed dose-dependent drug synergy behavior to play a role in trial design and realizing positive treatment outcomes. IDentif.AI 2.0 represents an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

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Section: Introductionmentioning

confidence: 91%

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

Blasiak

Truong

Remus

et al. 2021

Preprint

Self Cite

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“…As demonstrated on a rare subtype of peripheral T-cell lymphoma (PTCL), hepatosplenic T-cell lymphoma, this machine learning-driven platform by KYAN has a relatively high concordance to previous clinical trials. 72 Before ex vivo analysis, the patient was administered the standard-of-care combinations (HyperCVAD, pembrolizumab, GVD [gemcitabine, vinorelbine, and liposomal doxorubicin], and pralatrexate), but the disease was refractory to them. Upon carrying out the ex vivo analysis as harnessed through KYAN's platform, panobinostat and bortezomib emerged as the optimal combination for the same patient.…”

Section: Application Of Ai In Biomarker Discoverymentioning

confidence: 99%

“…These data could be derived experimentally at the in vitro, in vivo, and ex vivo stages to rationally identify the most optimal drug combination. This platform has been leveraged on many indications including multiple myeloma 71 and T-cell lymphoma, 72 for which epigenetic drugs have been repurposed for these hematological disorders. Recently, the platform has also repositioned antiviral drugs to inhibit SARS-CoV-2.…”

Section: Application Of Ai In Drug Repositioningmentioning

confidence: 99%

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

Rashid¹

2021

SLAS Technology

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“…These outcomes, coupled with the fact that foundational precursors to IDentif.AI have been clinically validated for infectious disease, oncology, and organ transplantation human studies, support the potential application of IDentif.AI as a clinical decision support platform for the optimized design of actionable combination therapy regimens. [14][15][16] 2 | RESULTS…”

Section: Introductionmentioning

confidence: 99%

IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐Cov‐2) with digital drug development

Blasiak

Lim

Seah

et al. 2020

Bioengineering & Transla Med

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The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization

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Application of an ex-vivo drug sensitivity platform towards achieving complete remission in a refractory T-cell lymphoma

Cited by 27 publications

References 15 publications

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

The IDentif.AI 2.0 Pandemic Readiness Platform: Rapid Prioritization of Optimized COVID-19 Combination Therapy Regimens

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

IDentif.AI: Rapidly optimizing combination therapy design against severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐Cov‐2) with digital drug development

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