Prevalence and Significance of Potential Pharmacokinetic Drug–Drug Interactions Among Patients with Lung Cancer: Implications for Clinical Trials

Rashdan, Sawsan; Yang, Hui; Le, Thanh; Selby, Christopher; Gerber, David E.

doi:10.1007/s40261-020-00994-4

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…Some working group participants urged caution regarding concomitant medication-related eligibility (eg, drug-drug interactions, QTc prolongation risk) given the frequency and unclear clinical significance of these issues in clinical practice. 24,25…”

Section: Othermentioning

confidence: 99%

“…Examples considered by the group included reproductive status, autoimmune disease for immunotherapy, bleeding/clotting for antiangiogenic therapies, active infection for cytotoxic/immunosuppressive therapies, and drug-drug interactions. Some working group participants urged caution regarding concomitant medication-related eligibility (eg, drug-drug interactions, QTc prolongation risk) given the frequency and unclear clinical significance of these issues in clinical practice …”

Section: Recommendationsmentioning

confidence: 99%

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A New Approach to Simplifying and Harmonizing Cancer Clinical Trials—Standardizing Eligibility Criteria

et al. 2022

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ancer clinical trial accrual remains a persistent challenge. Difficulties recruiting and retaining representative participants result in prolonged study duration, premature trial closure, heightened costs of clinical research, and delayed advances in the field. Many oncology trials close prematurely, and those that do finish are generally extremely costly. Multiple factors contribute to these problems, including complexity of trial protocols, lack of access to trial sites, and stringent eligibility criteria. 1 Clinical trial sponsors rely on eligibility criteria to control the characteristics of study participants to generate interpretable and reproducible results. Over the past several decades, however, the number and specificity of eligibility criteria have increased substantially, further slowing accrual and limiting generalizability of study findings to real-world populations. 2 In response to these trends, several multistakeholder efforts have sought to modernize eligibility criteria to ensure that more patients can access therapies in development. [3][4][5][6][7][8][9] These activities focused primarily on reforming approaches to developing clinical trial eligibility criteria. LUNGevity Foundation (hereafter, LUNGevity), a nonprofit patient advocacy or-ganization committed to increasing quality of life and survivorship of people with lung cancer, formed a scientific and clinical research roundtable initiative to streamline lung cancer clinical trials. To date, this group has issued 2 sets of recommendations regarding eligibility criteria. 10,11 To address the need for consistency in how clinical trial eligibility criteria are defined and listed in protocols, LUNGevity collaborated with the US Food and Drug Administration (FDA) and the National Cancer Institute (NCI) to address 3 challenges arising from confusing and inconsistent eligibility criteria in clinical trials for advanced-stage non-small cell lung cancer (NSCLC): (1) patients, caregivers, and clinicians often face difficulties navigating trial protocols to determine eligibility because publicly available trial search engines do not standardize the listing of inclusion and exclusion criteria; (2) increasingly numerous and stringent eligibility criteria exclude a growing proportion of patients; and (3) heterogeneous study populations render cross-trial comparisons difficult.Herein, we present the result of this effort: a prioritized list of recommended terms for inclusion and exclusion criteria in first-IMPORTANCE Clinical trial sponsors rely on eligibility criteria to control the characteristics of patients in their studies, promote the safety of participants, and optimize the interpretation of results. However, in recent years, complex and often overly restrictive inclusion and exclusion criteria have created substantial barriers to patient access to novel therapies, hindered trial recruitment and completion, and limited generalizability of trial results. A LUNGevity Foundation working group developed a framework for lung cancer clinical trial elig...

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Section: Othermentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

A New Approach to Simplifying and Harmonizing Cancer Clinical Trials—Standardizing Eligibility Criteria

et al. 2022

Self Cite

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“…Given the inhibiting and/or inducing effects that the majority of SMIs have on cytochrome P450 isoenzyme 3A4 (CYP3A4) or drug efflux pumps P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), SMIs may cause DDIs with DOACs [16], which result in either increased or decreased exposure to DOACs. These DDIs can be clinically relevant [17,18] as a clear relationship between systemic exposure to DOACS and the risk of bleeding events or VTEs has been well established [19][20][21][22]. Of note, DOACs are not expected to influence the efficacy and/or toxicity of the SMIs, so when it comes to DDI terminology, the SMIs are the perpetrators and the DOACs the victims of a potential DDI.…”

Section: Introductionmentioning

confidence: 99%

Practical recommendations to combine small-molecule inhibitors and direct oral anticoagulants in patients with nonsmall cell lung cancer

Otten¹,

Piet

Heuvel

et al. 2022

Eur Respir Rev

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BackgroundThe risk for thromboembolisms in nonsmall cell lung cancer (NSCLC) patients is increased and often requires treatment or prophylaxis with direct oral anticoagulants (DOACs). Small-molecule inhibitors (SMIs) to treat NSCLC may cause relevant drug–drug interactions (DDIs) with DOACs. Guidance on how to combine these drugs is lacking, leaving patients at risk of clotting or bleeding. Here, we give practical recommendations to manage these DDIs.MethodsFor all DOACs and SMIs approved in Europe and the USA up to December 2021, a literature review was executed and reviews by the US Food and Drug Administration and European Medicines Agency were analysed for information on DDIs. A DDI potency classification for DOACs was composed and brought together with DDI characteristics of each SMI, resulting in recommendations for each combination.ResultsHalf of the combinations result in relevant DDIs, requiring an intervention to prevent ineffective or toxic treatment with DOACs. These actions include dose adjustments, separation of administration or switching between anticoagulant therapies. Combinations of SMIs with edoxaban never cause relevant DDIs, compared to more than half of combinations with other DOACs and even increasing to almost all combinations with rivaroxaban.ConclusionsCombinations of SMIs and DOACs often result in relevant DDIs that can be prevented by adjusting the DOAC dosage, separation of administration or switching between anticoagulants.

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“…Clinical trials are effective solutions, but are labor-intensive and costly. With the development of artificial intelligence and the increase in the quantity of various data, many researchers have focused on the discovery of DDIs based on data-driven computational methods, thus saving manpower and material resources …”

Section: Introductionmentioning

confidence: 99%

“…With the development of artificial intelligence and the increase in the quantity of various data, many researchers have focused on the discovery of DDIs based on data-driven computational methods, thus saving manpower and material resources. 4 Machine learning methods, a commonly used data-driven computational approach, build prediction models based on drug features extracted from known DDIs to predict unobserved DDIs and DDI events. Most of these methods are based on an assumption of structural similarity, if drug 1 and drug 2 interact, drugs with a similar structure to drug 1 (or drug 2) also have similar interactions with drug 2 (or drug 1).…”

Section: Introductionmentioning

confidence: 99%

Multisource Attention-Mechanism-Based Encoder–Decoder Model for Predicting Drug–Drug Interaction Events

Pan

Quan

Jin

et al. 2022

J. Chem. Inf. Model.

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Many computational methods have been proposed to predict drug−drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder−decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network. Experiments showed that both MAEDDI and MAEDDIE performed better than some state-of-the-art methods in various validation attempts at different experimental tasks. The visualization analysis showed that the semantic features of drug pairs learned from our models had a good drug representation. In practice, MAEDDIE successfully screened 43 DDI events on favipiravir, an influenza antiviral drug, with a success rate of nearly 50%. Our model achieved competitive results, mainly owing to the design of sequence-based, structural, biochemical, and statistical multisource features. Moreover, different encoders constructed based on different features learn the interrelationship information between drug pairs, and the different representations of these drug pairs are incorporated to predict the target problem. All of these encoders were designed to better characterize the complex DDI relationships, allowing us to achieve high generalization in DDI and DDI-associated event predations.

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Prevalence and Significance of Potential Pharmacokinetic Drug–Drug Interactions Among Patients with Lung Cancer: Implications for Clinical Trials

Cited by 8 publications

References 22 publications

A New Approach to Simplifying and Harmonizing Cancer Clinical Trials—Standardizing Eligibility Criteria

A New Approach to Simplifying and Harmonizing Cancer Clinical Trials—Standardizing Eligibility Criteria

Practical recommendations to combine small-molecule inhibitors and direct oral anticoagulants in patients with nonsmall cell lung cancer

Multisource Attention-Mechanism-Based Encoder–Decoder Model for Predicting Drug–Drug Interaction Events

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