Purpose
To develop an automated treatment planning strategy for external beam intensity‐modulated radiation therapy (IMRT), including a deep learning‐based three‐dimensional (3D) dose prediction and ...a dose distribution‐based plan generation algorithm.
Methods and Materials
A residual neural network‐based deep learning model is trained to predict a dose distribution based on patient‐specific geometry and prescription dose. A total of 270 head‐and‐neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation.
Results
Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose–volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV70.4, but the difference is only 0.5% which is still clinically acceptable.
Conclusion
This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution‐based optimization. It is a promising approach for realizing automated treatment planning in the future.
Oral targeted therapies show a high pharmacokinetic (PK) interpatient variability. Even though exposure has been positively correlated with efficacy for many of these drugs, these are still dosed ...using a one-size-fits-all approach. Consequently, individuals have a high probability to be either underexposed or overexposed, potentially leading to suboptimal outcomes. Therapeutic drug monitoring, which is personalized dosing based on measured systemic drug concentrations, could address these problems.
Patients were enrolled in this prospective multicenter study (www.trialregister.nl; NL6695) if they started treatment with one of the 24 participating oral targeted therapies. Primary outcome was to halve the proportion of underexposed patients, compared with historical data. PK sampling was carried out after 4, 8 and 12 weeks, and every 12 weeks thereafter. In case of Cmin below the predefined target and manageable toxicity, a pharmacokinetically guided intervention was proposed (i.e. checking compliance and drug–drug interactions, concomitant intake with food, splitting intake moments or dose increments).
In total, 600 patients were included of whom 426 patients are assessable for the primary outcome and 552 patients had ≥1 PK sample(s) available and were therefore assessable for the overall analyses. Pharmacokinetically guided dosing reduced the proportion of underexposed patients at the third PK measurement by 39.0% (95% confidence interval 28.0% to 49.0%) compared with historical data. At the third PK measurement, 110 out of 426 patients (25.8%) had a low exposure. In total, 294 patients (53.3%) had ≥1 PK sample(s) below the preset target at a certain time point during treatment. In 166 of these patients (56.5%), pharmacokinetically guided interventions were carried out, which were successful in 113 out of 152 assessable patients (74.3%).
Pharmacokinetically guided dose optimization of oral targeted therapies was feasible in clinical practice and reduced the proportion of underexposed patients considerably.
•Half of the patients treated with oral targeted therapies are underexposed at a certain time point during treatment.•In the majority of the underexposed patients a dose adjustment could be carried out.•PK-guided dose adjustments were successful in the majority of patients without inducing additional toxicity.•PK-guided dose optimization of oral targeted therapies reduces the proportion of underexposed patients considerably.
Summary
The achievement of treatment‐free remission (TFR) has become a significant clinical end‐point in the management of patients with chronic myeloid leukaemia (CML), providing an opportunity to ...discontinue therapy with tyrosine kinase inhibitors (TKIs) while maintaining deep molecular response (DMR). Early studies, such as the French STIM trial, have demonstrated that a portion of patients can maintain DMR after treatment cessation, with rates ranging from 40% to 50%, and most relapses occurring within the first 6 months. Key prognostic factors for successful TFR, including treatment duration, duration of DMR, risk scores, and transcript type, have been identified. Optimal patient selection for TFR remains a challenge, but recent research provides insights into potential strategies to increase TFR eligibility. Evidence suggests that early intervention switching to achieve optimal response, treatment combinations, proactive switch in the case of absence of DMR, dose‐optimization and induction‐maintenance approach can improve molecular responses and, consequently, enhance TFR eligibility. In this review, we report and discuss all the potential therapeutic strategies that may enhance eligibility for a first attempt at TFR, with a particular emphasis on potential future approaches.
Treatment‐free remission (TFR) is pivotal in chronic myeloid leukemia (CML) management. Patient selection remains a challenge, but interventions like early switching to achieve optimal response, treatment combinations, switching strategies, dose optimization and induction‐maintenance approach hold promise for improving TFR eligibility.
Effective treatment of sepsis not only demands prompt administration of appropriate antimicrobials but also requires precise dosing to enhance the likelihood of patient survival. Adequate dosing ...refers to the administration of doses that yield therapeutic drug concentrations at the infection site. This ensures a favorable clinical and microbiological response while avoiding antibiotic-related toxicity. Therapeutic drug monitoring (TDM) is the recommended approach for attaining these goals. However, TDM is not universally available in all intensive care units (ICUs) and for all antimicrobial agents. In the absence of TDM, healthcare practitioners need to rely on several factors to make informed dosing decisions. These include the patient's clinical condition, causative pathogen, impact of organ dysfunction (requiring extracorporeal therapies), and physicochemical properties of the antimicrobials. In this context, the pharmacokinetics of antimicrobials vary considerably between different critically ill patients and within the same patient over the course of ICU stay. This variability underscores the need for individualized dosing. This review aimed to describe the main pathophysiological changes observed in critically ill patients and their impact on antimicrobial drug dosing decisions. It also aimed to provide essential practical recommendations that may aid clinicians in optimizing antimicrobial therapy among critically ill patients.
Purpose
Empirical relative biological effectiveness (RBE) models have been used to estimate the biological dose in proton therapy but do not adequately capture the factors influencing RBE values for ...treatment planning. We reformulate the McNamara RBE model such that it can be added as a linear biological dose fidelity term within our previously developed sensitivity‐regularized and heterogeneity‐weighted beam orientation optimization (SHBOO) framework.
Methods
Based on our SHBOO framework, we formulated the biological optimization problem to minimize total McNamara RBE dose to OARs. We solve this problem using two optimization algorithms: FISTA (McNam‐FISTA) and Chambolle‐Pock (McNam‐CP). We compare their performances with a physical dose optimizer assuming RBE = 1.1 in all structures (PHYS‐FISTA) and an LET‐weighted dose model (LET‐FISTA). Three head and neck patients were planned with the four techniques and compared on dosimetry and robustness.
Results
Compared to Phys‐FISTA, McNam‐CP was able to match CTV HI, Dmax, D95%, D98% by 0.00, 0.05%, 1.4%, 0.8%. McNam‐FISTA and McNam‐CP were able to significantly improve overall OAR Dmean, Dmax by an average of 36.1%,26.4% and 29.6%, 20.3%, respectively. Regarding CTV robustness, worst Dmax, V95%, D95%, D98% improvement of −6.6%, 6.2%, 6.0%, 4.8% was reported for McNam‐FISTA and 2.7%, 2.7%, 5.3%, −4.3% for McNam‐CP under combinations of range and setup uncertainties. For OARs, worst Dmax, Dmean were improved by McNam‐FISTA and McNam‐CP by an average of 25.0%, 19.2% and 29.5%, 36.5%, respectively. McNam‐FISTA considerably improved dosimetry and CTV robustness compared to LET‐FISTA, which achieved better worst‐case OAR doses.
Conclusion
The four optimization techniques deliver comparable biological doses for the head and neck cases. Besides modest CTV coverage and robustness improvement, OAR biological dose and robustness were substantially improved with both McNam‐FISTA and McNam‐CP, showing potential benefit for directly incorporating McNamara RBE in proton treatment planning.
Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone ...marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens.
Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant.
The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects).
A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs.
Soil salinity is progressively affecting global agriculture area, and act as a brutal environmental factor for the productivity of plants, therefore, sustainable remediation of the saline soil is ...urgently required. In this study, we tested the effectiveness of PM (poultry manure), SMS (spent mushroom substrate), and CD (cow dung) for the recovery of salt soil and the optimization of the productivity of the maize plant. PM and SMS showed the valuable source of OC, N, P, K as the CD. The HCA analysis showed that 47% of the bacterial population from PM, SMS, and CD survived at 6% NaCl (w/v), which had PGP attributes such as IAA, P-solubilizers, and siderophore activity. The results from pot experiments of plant growth and PCA analysis of bacterial PGP attributes reveled re formulation of PM, SMS, and CD, which were further optimized at the saline field level. T-2 treated plant increased their shoot length, chlorophyll content, reducing sugar, nitrogen, phosphorus, and potassium levels significantly after 30 and 60 days, followed by T-4 and T-3 as the control. A significant (P < 0.01) increase in particle density and decrease in bulk density was observed for all combinations treated (T-2 to T-7). A two-year field study revealed that the T-2 combination increased 43% OC, 57% N, 66% P, 48% K, 32% DHA, 76% PPO in the soil than the control after 60 days. T2-combination decreased ≈50% of Na content in root and shoot, and increased 27% of maize crop yield. The dose of 10% PM + 10% SMS can significantly induce the growth of maize plants and the restoration of saline soil health.
•PM (poultry manure) and SMS (spent mushroom substrate) showed the rich source of nutrients and salt tolerant PGPB.•PCA analysis of plant growth parameters and screening of PM, SMS, and CD (cow dung) for saline soil had a remarkable step for dose-optimization.•T-2 treated combination (10% PM and 10% SMS) significantly induced maize plant growth and yield.•T-2 combination increased 43% OC, 57% N, 66% P, 48% K, 32% DHA, 76% PPO in saline soil and decreased 50% Na uptake in plant as control.
Cytotoxic chemotherapeutics form the cornerstone of systemic treatment of many cancers. Patients are dosed at maximum tolerated dose (MTD), which is carefully determined in phase I studies. In ...contrast, in murine studies, dosages are often based on customary practice or small pilot studies, which often are not well documented. Consequently, research groups need to replicate experiments, resulting in an excess use of animals and highly variable dosages across the literature. In addition, while patients often receive supportive treatments in order to allow dose escalation, mice do not. These issues could affect experimental results and hence clinical translation.
To address this, we determined the single-dose MTD in BALB/c and C57BL/6 mice for a range of chemotherapeutics covering the canonical classes, with clinical score and weight as endpoints.
We found that there was some variation in MTDs between strains and the tolerability of repeated cycles of chemotherapy at MTD was drug-dependent. We also demonstrate that dexamethasone reduces chemotherapy-induced weight loss in mice.
These data form a resource for future studies using chemotherapy in mice, increasing comparability between studies, reducing the number of mice needed for dose optimisation experiments and potentially improving translation to the clinic.
In 2021, the Food and Drug Administration Oncology Center of Excellence announced Project Optimus focusing on dose optimization for oncology drugs. The Methodology for the Development of Innovative ...Cancer Therapies (MDICT) Taskforce met to review and discuss the optimization of dosage for oncology trials and to develop a practical guide for oncology phase I trials. Defining a single recommended phase II dose based on toxicity may define doses that are neither the most effective nor the best tolerated. MDICT recommendations address the need for robust non-clinical data which are needed to inform trial design, as well as an expert team including statisticians and pharmacologists. The protocol must be flexible and adaptive, with clear definition of all endpoints. Health authorities should be consulted early and regularly. Strategies such as randomization, intrapatient dose escalation, and real-world eligibility criteria are encouraged whereas serial tumor sampling is discouraged in the absence of a strong rationale and appropriately validated assay. Endpoints should include consideration of all longitudinal toxicity. The phase I dose escalation trial should define the recommended dose range for later testing in randomized phase II trials, rather than a single recommended phase II dose, and consider scenarios where different populations may require different dosages. The adoption of these recommendations will improve dosage selection in early clinical trials of new anticancer treatments and ultimately, outcomes for patients.
•Recent experience with targeted anticancer drugs and immunotherapies suggest the traditional approach of using the maximal tolerated dose (MTD) is not ideal.•Project Optimus recommends optimizing the dosage of oncology drugs to ensure they are both effective and tolerable.•MDICT developed practical recommendations for the design and conduct of phase I oncology trials.
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