The Physical and Radiobiological Basis of the Local Effect Model:A Response to the Commentary by R. Katz

Seventy Years of Dose-response Models: From the Target Theory to the Use of Big Databases Involving Cell Survival and DNA Repair

Radiobiological data, whether obtained at the clinical, biological or molecular level has significantly contributed to a better description and prediction of the individual dose-response to ionizing radiation and a better estimation of the radiation-induced risks. Particularly, over the last seventy years, the amount of radiobiological data has considerably increased, and permitted the mathematical formulas describing dose-response to become less empirical. A better understanding of the basic radiobiological mechanisms has also contributed to establish quantitative inter-correlations between clinical, biological and molecular biomarkers, refining again the mathematical models of description. Today, big data approaches and, more recently, artificial intelligence may finally complete and secure this long process of thinking from the multi-scale description of radiation-induced events to their prediction. Here, we reviewed the major dose-response models applied in radiobiology for quantifying molecular and cellular radiosensitivity and aimed to explain their evolution: Specifically, we highlighted the advances concerning the target theory with the cell survival models and the progressive introduction of the DNA repair process in the mathematical models. Furthermore, we described how the technological advances have changed the description of DNA double-strand break (DSB) repair kinetics by introducing the important notion of DSB recognition, independent of that of DSB repair. Initially developed separately, target theory on one hand and, DSB recognition and repair, on the other hand may be now fused into a unified model involving the cascade of phosphorylations mediated by the ATM kinase in response to any genotoxic stress.

Modelling Cellular Response to Ionizing Radiation: Mechanistic, Semi-Mechanistic, and Phenomenological Approaches - A Historical Perspective

Radiation research is a multidisciplinary field, and among its many branches, mathematical and computational modelers have played a significant role in advancing boundaries of knowledge. A fundamental contribution is modelling cellular response to ionizing radiation as that is the key to not only understanding how radiation can kill cancer cells, but also cause cancer and other health issues. The invention of microdosimetry in the 1950s by Harold Rossi paved the way for brilliant scientists to study the mechanism of radiation at cellular and sub-cellular scales. This paper reviews some snippets of ingenious mathematical and computational models published in microdosimetry symposium proceedings and publications of the radiation research community. Among these are simulations of radiation tracks at atomic and molecular levels using Monte Carlo methods, models of cell survival, quantification of the amount of energy required to create a single strand break, and models of DNA-damage-repair. These models can broadly be categorized into mechanistic, semi-mechanistic, and phenomenological approaches, and this review seeks to provide historical context of their development. We salute pioneers of the field and great teachers who supported and educated the younger members of the community and showed them how to build upon their work.

RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/β values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not β. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and β coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Clonogenic assay and computational modeling using real cell images to study physical enhancement and cellular sensitization induced by metal nanoparticles under MV and kV X-ray irradiation

This study was initiated due to the physically unexplainable tumor controls resulting from metal nanoparticle (MNP) experiments even under MV X-ray irradiation. A more accurate explanation of the mechanism of radiosensitization induced by MNP is warranted, considering both its physical dose enhancement and biological sensitization, as related research is lacking. Thus, we aimed to examine the intricate dynamics involved in MNP-induced radiosensitization. We conducted specifically designed clonogenic assays for the A549 lung cancer cell line with MNP irradiated by 6 MV and 300 kVp X-rays. Two types of MNP were employed: one based on iron oxide, promoting ferroptosis, and the other on gold nanoparticles known for inducing a significant dose enhancement, particularly at low-energy X-rays. We introduced the lethality enhancement factor (LEF) as the fraction in the cell killing attributed to biological sensitization. Subsequently, Monte Carlo simulations were conducted to evaluate the radial dose profiles for each MNP, corresponding to the physical enhancement. Finally, the local effect model was applied to the clonogenic assay results on real cell images. The LEF and the dose enhancement in the cytoplasm were incorporated to increase the accuracy in the average lethal events and, consequently, in the survival fraction. The results reveal an increased cell killing for both of the MNP under MV and kV X-ray irradiation. In both types of MNP, the LEF reveals a biological sensitization evident. The sensitizer enhancement ratio, derived from the calculations, exhibited only 3% and 1% relative differences compared to the conventional linear-quadratic model for gold and ferroptosis inducer nanoparticles, respectively. These findings indicate that MNPs sensitize cells via radiation through mechanisms akin to ferroptosis inducers, not exclusively relying on a physical dose enhancement. Their own contributions to survival fractions were successfully integrated into computational modeling.

Effects of nuclear interaction corrections and trichrome fragment spectra modelling on dose and linear energy transfer distributions in carbon ion radiotherapy

Background and Purpose

Nuclear interaction correction (NIC) and trichrome fragment spectra modelling improve relative biological effectiveness-weighted dose (DRBE) and dose-averaged linear energy transfer (LETd) calculation for carbon ions. The effect of those novel approaches on the clinical dose and LET distributions was investigated.

Materials and Methods

The effect of the NIC and trichrome algorithm was assessed, creating single beam plans for a virtual water phantom with standard settings and NIC + trichrome corrections. Reference DRBE and LETd distributions were simulated using FLUKA version 2021.2.9. Thirty clinically applied scanned carbon ion treatment plans were recalculated applying NIC, trichrome and NIC + trichrome corrections, using the LEM low dose approximation and compared to clinical plans (base RS). Four treatment sites were analysed: six prostate adenocarcinoma, ten head and neck, nine locally advanced pancreatic adenocarcinoma and five sacral chordoma. The FLUKA and clinical plans were compared in terms of DRBE deviations for D98%, D50%, D2% for the clinical target volume (CTV) and D50% in ring-like dose regions retrieved from isodose curves in base RS plans. Additionally, region-based median LETd deviations and global gamma parameters were evaluated.

Results

Dose deviations comparing base RS and evaluation plans were within ± 1% supported by γ-pass rates over 97% for all cases. No significant LETd deviations were reported in the CTV, but significant median LETd deviations were up to 80% for very low dose regions.

Conclusion

Our results showed improved accuracy of the predicted DRBE and LETd. Considering clinically relevant constraints, no significant modifications of clinical protocols are expected with the introduction of NIC + trichrome.

National Effort to Re-Establish Heavy Ion Cancer Therapy in the United States

In this review, we attempt to make a case for the establishment of a limited number of heavy ion cancer research and treatment facilities in the United States. Based on the basic physics and biology research, conducted largely in Japan and Germany, and early phase clinical trials involving a relatively small number of patients, we believe that heavy ions have a considerably greater potential to enhance the therapeutic ratio for many cancer types compared to conventional X-ray and proton radiotherapy. Moreover, with ongoing technological developments and with research in physical, biological, immunological, and clinical aspects, it is quite plausible that cost effectiveness of radiotherapy with heavier ions can be substantially improved.

Biological effectiveness and relative biological effectiveness of ion beams for in-vitro cell irradiation

Biological effectiveness and relative biological effectiveness are critical for proton and ion beam radiotherapy. However, the relationship between the two quantities and physical character of ion beams is not well established. By analyzing 1188 sets of in‐vitro cell irradiation experiments using ion beams ranging from protons to ²³⁸U, compiled by the Particle Irradiation Data Ensemble (PIDE) project, the biological effectiveness of the ion beams, with cell survival fractionation (SF) as the endpoint, was found to be dependent on the fluence and linear energy transfer (LET) of the ion beam. Consequently, the relative biological effectiveness of the ion beam to photon beam was also established as a function of LET. A common form of relationship among SF, fluence, and LET was found to be valid for all ion beam experiments. The close form relationship could be used for proton and ion beam radiotherapy applications.

Monte Carlo simulations of nanodosimetry and radiolytic species production for monoenergetic proton and electron beams: Benchmarking of GEANT4-DNA and LPCHEM codes

PURPOSE

In hadrontherapy, biophysical models can be used to predict the biological effect received by cancerous tissues and organs at risk. The input data of these models generally consist of information on nano/micro dosimetric quantities and, concerning some models, reactive species produced in water radiolysis. In order to fully account for the radiation stochastic effects, these input data have to be provided by Monte Carlo track structure (MCTS) codes allowing to estimate physical, physico-chemical, and chemical effects of radiation at the molecular scale. The objective of this study is to benchmark two MCTS codes, Geant4-DNA and LPCHEM, that are useful codes for estimating the biological effects of ions during radiation therapy treatments.

MATERIAL AND METHODS

In this study we considered the simulation of specific energy spectra for monoenergetic proton beams (10 MeV) as well as radiolysis species production for both electron (1 MeV) and proton (10 MeV) beams with Geant4-DNA and LPCHEM codes. Options 2, 4, and 6 of the Geant4-DNA physics lists have been benchmarked against LPCHEM. We compared probability distributions of energy transfer points in cylindrical nanometric targets (10 nm) positioned in a liquid water box. Then, radiochemical species (· OH, e aq - ${\rm{e}}_{{\rm{aq}}}^ - $ , H 3 O + , H 2 O 2 ${{\rm{H}}_3}{{\rm{O}}^ + },{\rm{\;}}{{\rm{H}}_2}{{\rm{O}}_2}$ , H₂ , and O H - ) ${\rm{O}}{{\rm{H}}^ - }){\rm{\;}}$ yields simulated between 10^-12 and 10^-6 s after irradiation are compared.

RESULTS

Overall, the specific energy spectra and the chemical yields obtained by the two codes are in good agreement considering the uncertainties on experimental data used to calibrate the parameters of the MCTS codes. For 10 MeV proton beams, ionization and excitation processes are the major contributors to the specific energy deposition (larger than 90%) while attachment, solvation, and vibration processes are minor contributors. LPCHEM simulates tracks with slightly more concentrated energy depositions than Geant4-DNA which translates into slightly faster recombination than Geant4-DNA. Relative deviations (C_EV ) with respect to the average of evolution rates of the radical yields between 10^-12 and 10^-6 s remain below 10%. When comparing execution times between the codes, we showed that LPCHEM is faster than Geant4-DNA by a factor of about four for 1000 primary particles in all simulation stages (physical, physico-chemical, and chemical). In multi-thread mode (four threads), Geant4-DNA computing times are reduced but remain slower than LPCHEM by ∼20% up to ∼50%.

CONCLUSIONS

For the first time, the entire physical, physico-chemical, and chemical models of two track structure Monte Carlo codes have been benchmarked along with an extensive analysis on the effects on the water radiolysis simulation. This study opens up new perspectives in using specific energy distributions and radiolytic species yields from monoenergetic ions in biophysical models integrated to Monte Carlo software.

Modeling Radiation-Induced Neoplastic Cell Transformation In Vitro and Tumor Induction In Vivo with the Local Effect Model

Ionizing radiation induces DNA damage to cycling cells which, if left unrepaired or misrepaired, can cause cell inactivation or heritable, viable mutations. The latter can lead to cell transformation, which is thought to be an initial step of cancer formation. Consequently, the study of radiation-induced cell transformation promises to offer insights into the general properties of radiation carcinogenesis. As for other end points, the effectiveness in inducing cell transformation is elevated for radiation qualities with high linear energy transfer (LET), and the same is true for cancer induction. In considering DNA damage as a common cause of both cell death and transformations, a worthwhile approach is to apply mathematical models for the relative biological effectiveness (RBE) of cell killing to also assess the carcinogenic potential of high-LET radiation. In this work we used an established RBE model for cell survival and clinical end points, the local effect model (LEM), to estimate the transformation probability and the carcinogenic potential of ion radiation. The provided method consists of accounting for the competing processes of cell inactivation and induction of transformations or carcinogenic events after radiation exposure by a dual use of the LEM. Correlations between both processes inferred by the number of particle impacts to individual cells were considered by summing over the distribution of hits that individual cells receive. RBE values for cell transformation in vitro were simulated for three independent data sets, which were also used to gauge the approach. The simulations reflect the general RBE systematics both in magnitude and in energy and LET dependence. To challenge the developed method, in vivo carcinogenesis was investigated using the same concepts, where the probability for cancer induction within an irradiated organ was derived from the probability of finding carcinogenic events in individual cells. The predictions were compared with experimental data of carcinogenesis in Harderian glands of mice. Again, the developed method shows the same characteristics as the experimental data. We conclude that the presented method is helpful to predictively assess RBE for both neoplastic cell transformation and tumor induction after ion exposure within a wide range of LET values. The theoretical concept requires a non-linear component in the photon dose response for carcinogenic end points as a precondition for the observed enhanced effects after ion exposure, thus contributing to a long debate in epidemiology. Future work will use the method for assessing cancer induction in radiation therapy and exposure scenarios frequently discussed in radiation protection.

Advances in modelling gold nanoparticle radiosensitization using new Geant4-DNA physics models

Gold nanoparticles have demonstrated significant radiosensitization of cancer treatment with x-ray radiotherapy. To understand the mechanisms at the basis of nanoparticle radiosensitization, Monte Carlo simulations are used to investigate the dose enhancement, given a certain nanoparticle concentration and distribution in the biological medium. Earlier studies have ordinarily used condensed history physics models to predict nanoscale dose enhancement with nanoparticles. This study uses Geant4-DNA complemented with novel track structure physics models to accurately describe electron interactions in gold and to calculate the dose surrounding gold nanoparticle structures at nanoscale level. The computed dose in silico due to a clinical kilovoltage beam and the presence of gold nanoparticles was related to in vitro brain cancer cell survival using the local effect model. The comparison of the simulation results with radiobiological experimental measurements shows that Geant4-DNA and local effect model can be used to predict cell survival in silico in the case of x-ray kilovoltage beams.

Determination of the Effective Local Lethal Function for the NanOx Model

NanOx is a biophysical model recently developed in the context of hadrontherapy to predict the cell survival probability from ionizing radiation. It postulates that this may be factorized into two independent terms describing the cell response to two classes of biological events that occur in the sequence of an irradiation: the local lethal events that occur at nanometric scale and can by themselves induce cell death, and the non-local lethal events that lead to cell death by an effect of accumulation and/or interaction at a larger scale. Here we address how local lethal events are modeled in terms of the inactivation of undifferentiated nanometric targets via an "effective local lethal function F", which characterizes the response of each cell line to the spectra of "restricted specific energy". F is initially determined as a linear combination of basis functions. Then, a parametric expression is used to reproduce the function's main features, a threshold and a saturation, while at the same time reducing the number of free parameters. This strategy was applied to three cell lines in response to ions of different type and energy, which allows for benchmarking of the α(LET) curves predicted with both effective local lethal functions against the experimental data.

Evolution of Carbon Ion Radiotherapy at the National Institute of Radiological Sciences in Japan

Charged particles can achieve better dose distribution and higher biological effectiveness compared to photon radiotherapy. Carbon ions are considered an optimal candidate for cancer treatment using particles. The National Institute of Radiological Sciences (NIRS) in Chiba, Japan was the first radiotherapy hospital dedicated for carbon ion treatments in the world. Since its establishment in 1994, the NIRS has pioneered this therapy with more than 69 clinical trials so far, and hundreds of ancillary projects in physics and radiobiology. In this review, we will discuss the evolution of carbon ion radiotherapy at the NIRS and some of the current and future projects in the field.

Proceedings of the National Cancer Institute Workshop on Charged Particle Radiobiology

In April 2016, the National Cancer Institute hosted a multidisciplinary workshop to discuss the current knowledge of the radiobiological aspects of charged particles used in cancer therapy to identify gaps in that knowledge that might hinder the effective clinical use of charged particles and to propose research that could help fill those gaps. The workshop was organized into 10 topics ranging from biophysical models to clinical trials and included treatment optimization, relative biological effectiveness of tumors and normal tissues, hypofractionation with particles, combination with immunotherapy, "omics," hypoxia, and particle-induced second malignancies. Given that the most commonly used charged particle in the clinic currently is protons, much of the discussion revolved around evaluating the state of knowledge and current practice of using a relative biological effectiveness of 1.1 for protons. Discussion also included the potential advantages of heavier ions, notably carbon ions, because of their increased biological effectiveness, especially for tumors frequently considered to be radiation resistant, increased effectiveness in hypoxic cells, and potential for differentially altering immune responses. The participants identified a large number of research areas in which information is needed to inform the most effective use of charged particles in the future in clinical radiation therapy. This unique form of radiation therapy holds great promise for improving cancer treatment.

A local effect model-based interpolation framework for experimental nanoparticle radiosensitisation data

A local effect model (LEM)-based framework capable of interpolating nanoparticle-enhanced photon-irradiated clonogenic cell survival fraction measurements as a function of nanoparticle concentration was developed and experimentally benchmarked for gold nanoparticle (AuNP)-doped bovine aortic endothelial cells (BAECs) under superficial kilovoltage X-ray irradiation. For three different superficial kilovoltage X-ray spectra, the BAEC survival fraction response was predicted for two different AuNP concentrations and compared to experimental data. The ability of the developed framework to predict the cell survival fraction trends is analysed and discussed. This developed framework is intended to fill in the existing gaps of individual cell line response as a function of NP concentration under photon irradiation and assist the scientific community in planning future pre-clinical trials of high Z nanoparticle-enhanced photon radiotherapy.

Fifteen symposia on microdosimetry: implications for modern particle-beam cancer radiotherapy

The objective of microdosimetry was, and still is, to identify physical descriptions of the initial physical processes of ionising radiation interacting with biological matter which correlate with observed radiobiological effects with a view to improve the understanding of radiobiological mechanisms and effects. The introduction of therapy with particles starting with fast neutrons followed by negative pions, protons and light ions necessitated the application of biological weighting factors for absorbed dose in order to account for differences of the relative biological effectiveness (RBE). Dedicated radiobiological experiments in therapy beams with mammalian cells and with laboratory animals provided sets of RBE values which are used to evaluate empirical 'clinical RBE values'. The combination of such experiments with microdosimetric measurements in identical conditions offered the possibility to establish semi-empirical relationships between microdosimetric parameters and results of RBE studies.

The effect of microdosimetric 12C6+ heavy ion irradiation and Mg2+ on canthaxanthin production in a novel strain of Dietzia natronolimnaea

Background

Dietzia natronolimnaea is one of the most important bacterial bioresources for high efficiency canthaxanthin production. It produces the robust and stable pigment canthaxanthin, which is of special interest for the development of integrated biorefineries. Mutagenesis employing ¹²C⁶⁺ irradiation is a novel technique commonly used to improve microorganism productivity. This study presents a promising route to obtaining the highest feasible levels of biomass dry weight (BDW), and total canthaxanthin by using a microdosimetric model of ¹²C⁶⁺ irradiation mutation in combination with the optimization of nutrient medium components.

Results

This work characterized the rate of both lethal and non-lethal dose mutations for ¹²C⁶⁺ irradiation and the microdosimetric kinetic model using the model organism, D. natronolimnaea svgcc1.2736. Irradiation with ¹²C⁶⁺ ions resulted in enhanced production of canthaxanthin, and is therefore an effective method for strain improvement of D. natronolimnaea svgcc1.2736. Based on these results an optimal dose of 0.5–4.5 Gy, Linear energy transfer (LET) of 80 keV μm^-1and energy of 60 MeV u^-1 for ¹²C⁶⁺ irradiation are ideal for optimum and specific production of canthaxanthin in the bacterium. Second-order empirical calculations displaying high R-squared (0.996) values between the responses and independent variables were derived from validation experiments using response surface methodology. The highest canthaxanthin yield (8.14 mg) was obtained with an optimized growth medium containing 21.5 g L^-1 D-glucose, 23.5 g L^-1 mannose and 25 ppm Mg²⁺ in 1 L with an irradiation dose of 4.5 Gy.

Conclusions

The microdosimetric ¹²C⁶⁺ irradiation model was an effective mutagenic technique for the strain improvement of D. natronolimnaea svgcc1.2736 specifically for enhanced canthaxanthin production. At the very least, random mutagenesis methods using ¹²C⁶⁺ions can be used as a first step in a combined approach with long-term continuous fermentation processes. Central composite design-response surface methodologies (CCD-RSM) were carried out to optimize the conditions for canthaxanthin yield. It was discovered D-glucose, Mg²⁺ and mannose have significant influence on canthaxanthin biosynthesis and growth of the mutant strain.

Initial recombination in the track of heavy charged particles: numerical solution for air filled ionization chambers

INTRODUCTION

Modern particle therapy facilities enable sub-millimeter precision in dose deposition. Here, also ionization chambers (ICs) are used, which requires knowledge of the recombination effects. Up to now, recombination is corrected using phenomenological approaches for practical reasons. In this study the effect of the underlying dose distribution on columnar recombination, a quantitative model for initial recombination, is investigated.

MATERIAL AND METHODS

Jaffé's theory, formulated in 1913 quantifies initial recombination by elemental processes, providing an analytical (closed) solution. Here, we investigate the effect of the underlying charged carrier distribution around a carbon ion track. The fundamental partial differential equation, formulated by Jaffé, is solved numerically taking into account more realistic charge carrier distributions by the use of a computer program (Gascoigne 3D). The investigated charge carrier distributions are based on track structure models, which follow a 1/r(2) behavior at larger radii and show a constant value at small radii. The results of the calculations are compared to the initial formulation and to data obtained in experiments using carbon ion beams.

RESULTS

The comparison between the experimental data and the calculations shows that the initial approach made by Jaffé is able to reproduce the effects of initial recombination. The amorphous track structure based charge carrier distribution does not reproduce the experimental data well. A small additional correction in the assessment of the saturation current or charge is suggested by the data.

CONCLUSION

The established model of columnar recombination reproduces the experimental data well, whereas the extensions using track structure models do not show such an agreement. Additionally, the effect of initial recombination on the saturation curve (i.e. Jaffé plot) does not follow a linear behavior as suggested by current dosimetry protocols, therefore higher order corrections (such as the investigated ones) might be necessary.

Cluster Effects within the Local Effect Model

The local effect model predicts the relative biological effectiveness (RBE) for different ions and cell lines starting from the corresponding experimental photon data and an amorphous track structure model. Here we present an extension of the model that takes cluster effects of single-strand breaks (SSBs) at the nanometer scale into account. In line with the main idea of the local effect model, we take the yields of SSBs and double-strand breaks (DSBs) from experimental photon data and use a Monte Carlo method to distribute them onto the DNA. We score clusters of SSBs where individual SSBs are separated by less than 25 bp as additional DSBs. Assuming that the number of DSBs is a measure of cell lethality, we derive a modified cell survival curve for photons that takes these cluster effects into account. In combination with an improved radial dose distribution, we find that the extended local effect model including cluster effects reproduces most experimental data better than the original local effect model and thus enhances the accuracy of the local effect model.

Comparison of the Methods of Specifying Carbon Ion Doses at NIRS and GSI

Due to the RBE variations, the carbon-ion doses (in Gy) are no longer sufficient to monitor adequately the biological effect of these radiations. Therefore, "RBE dose weighting factors" - W(RBE) - allowing for the RBE variations with energy, dose and biological system have to be introduced in the treatment plans in order to provide the physician with interpretable information. This paper compares the methods employed for this purpose at NIRS and GSI, which are specific of the beam delivery system of these institutions. NIRS has a "passive" beam delivery system where the dose distribution in the SOBP is determined by a Ridge filter. The dose distribution - and thus, the shaping of the filter - is chosen according to the clinical situation and determined with respect to W(RBE) factors in order to yield a biologically iso-effective SOBP. W(RBE )factors in the SOBP are at first derived from a RBE/LET function for HSG cells, then normalized to 3 at a LET of 80 keV/mum. The latter value of 3 corresponds to the clinical RBE of NIRS-neutrons, which were found to exhibit the same radiobiological properties as 80 keV/mum carbon-ions. GSI has a "dynamic" beam delivery system ("spot" or "voxel" scanning) making it possible to irradiate irregular volumes and to modulate the radiation intensity according to the radiosensitivity of different tissues and/or different sub-volumes. Due to the "power" and the resulting complexity of the system, W(RBE )factors are determined through an integrated calculation code allowing iterative interaction of both physical and radiobiological parameters. The "Local Effect Model" (LEM) was developed in this view with the aim of deriving carbon-ion W(RBE )factors from the parameters determining the response to photons. Advantages and weaknesses of the respective methods will be discussed.