The biological effectiveness of targeted radionuclide therapy based on a whole-body pharmacokinetic model

Murine-specific Internal Dosimetry for Preclinical Investigations of Imaging and Therapeutic Agents

There is a growing need to estimate the absorbed dose to small animals from preclinical investigations involving diagnostic and therapeutic radiopharmaceuticals. This paper introduces a Monte Carlo-based dosimetry platform called RAPID, which is capable of calculating murine-specific three-dimensional (3D) dose distributions. A comparison is performed between absorbed doses calculated with RAPID and absorbed doses calculated in a commonly used reference mouse phantom called MOBY. Four test mice containing different xenografts underwent serial PET/CT imaging using a novel diagnostic therapy (theranostic) agent NM404, which can be labeled with I for imaging or I for therapy. Using the PET/CT data, 3D dose distributions from I-NM404 were calculated in the mice using RAPID. Mean organ doses in these four test mice were compared to mean organ doses derived by using two previously published I S-values datasets in MOBY. In addition, mean tumor doses calculated in RAPID were compared to mean organ doses derived from unit density spheres. Large differences were identified between mean organ doses calculated in the test mice using RAPID and those derived in the MOBY phantom. Mean absorbed dose percent errors in organs ranged between 0.3% and 333%. Overall, mass scaling improved agreement between MOBY phantom calculations and RAPID, where percent errors were all less than 26%, with the exception of the lung in which percent errors reached values of 48%. Percent errors in mean tumor doses in the test mice and unit density spheres were less pronounced but still ranged between 8% and 23%. This work demonstrates the limitations of using pre-computed S-values in computational phantoms to predict organ doses in small animals from theranostic procedures. RAPID can generate accurate 3D dose distributions in small animals and in turn offer much greater insight on the ability of a given theranostic agent to image and treat diseases.

Integrated PK-PD and agent-based modeling in oncology

Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.

Optimization of vascular-targeting drugs in a computational model of tumor growth

A biophysical tool is introduced that seeks to provide a theoretical basis for helping drug design teams assess the most promising drug targets and design optimal treatment strategies. The tool is grounded in a previously validated computational model of the feedback that occurs between a growing tumor and the evolving vasculature. In this paper, the model is particularly used to explore the therapeutic effectiveness of two drugs that target the tumor vasculature: angiogenesis inhibitors (AIs) and vascular disrupting agents (VDAs). Using sensitivity analyses, the impact of VDA dosing parameters is explored, as is the effects of administering a VDA with an AI. Further, a stochastic optimization scheme is utilized to identify an optimal dosing schedule for treatment with an AI and a chemotherapeutic. The treatment regimen identified can successfully halt simulated tumor growth, even after the cessation of therapy.

Optimal preloading in radioimmunotherapy with anti-cD45 antibody

PURPOSE

Anti-CD45 antibody is predominantly used in the treatment of acute leukemia. CD45 is stably expressed on all leukocytes and their precursors, and therefore the liver and spleen constitute major antigen sinks. Thus, as the red marrow is the target organ, in radioimmunotherapy with anti-CD45 antibody, preloading with unlabeled antibody is a method to increase the absorbed dose to the target cells. In a previous study, a method to individually determine the optimal preload for five patients with acute leukemia was developed. Here, this method is examined and improved using two pretherapeutic measurement series and a refined pharmacokinetic model.

METHODS

To obtain the biodistribution of 111In-labeled anti-CD45 antibody under different saturation conditions, two measurement series one with and one without preloading were conducted in five patients. For each patient, two physiologically based pharmacokinetic models were fitted to the data and the corrected Akaike information criterion was used to identify the model, which was empirically most supported. The resultant parameter values were compared to values reported in the literature. To individually determine the optimal amount of unlabeled antibody for therapy, computer simulations for preloads ranging from 0 to 60 mg were performed based on the estimated parameters of each patient. The prediction power of the model was assessed by comparing the simulated therapeutic serum curves to the actual 90Y measurements.

RESULTS

Visual inspection showed good fits and the adjusted R2 was >0.90 for all patients. All parameters were in a physiologically reasonable range. The relative deviation of the predicted area under the therapeutic serum curve and the measured curve was 15%-33%. The optimal preloading increased the marrow-over-liver selectivity up to 3.9 fold compared to the simulated biodistribution using a standard dose (0.5 mg/kg).

CONCLUSIONS

The presented method can be used to individually determine the optimal preload and the corresponding residence times in radioimmunotherapy with anti-CD45 antibody.

Chemoradionuclide therapy with 186re-labeled liposomal doxorubicin: toxicity, dosimetry, and therapeutic response

This study was performed to determine the maximum tolerated dose (MTD) and therapeutic effects of rhenium-186 ((186)Re)-labeled liposomal doxorubicin (Doxil), investigate associated toxicities, and calculate radiation absorbed dose in head and neck tumor xenografts and normal organs. Doxil and control polyethylene glycol (PEG)-liposomes were labeled using (186)Re-N,N-bis(2-mercaptoethyl)-N',N'-diethylethylenediamine (BMEDA) method. Tumor-bearing rats received either no therapy (n=6), intravenous Doxil (n=4), or escalating radioactivity of (186)Re-Doxil (185-925 MBq/kg) or (186)Re-PEG-liposomes (1110-1665 MBq/kg) and were monitored for 28 days. Based on body weight loss and systemic toxicity, MTD for (186)Re-Doxil and (186)Re-PEG-liposomes were established at injected radioactivity/body weight of 740 and 1480 MBq/kg, respectively. (186)Re-injected radioactivity/body weight for therapy studies was determined to be 555 MBq/kg for (186)Re-Doxil and 1295 MBq/kg for (186)Re-PEG-liposomes. All groups recovered from their body weight loss, leucopenia, and thrombocytopenia by 28 days postinjection. Normalized radiation absorbed dose to tumor was significantly higher for (186)Re-Doxil (0.299±0.109 Gy/MBq) compared with (186)Re-PEG-liposomes (0.096±0.120 Gy/MBq) (p<0.05). In a separate therapy study, tumor volumes were significantly smaller for (186)Re-Doxil (555 MBq/kg) compared with (186)Re-PEG-liposomes (1295 MBq/kg) (p<0.01) at 42 days postinjection. In conclusion, combination chemoradionuclide therapy with (186)Re-Doxil has promising potential, because good tumor control was achieved with limited associated toxicity.