Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces could provide a promising basis for human motor augmentation by extracting potentially high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurons to effectively increase the number of degrees of freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N = 7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle  and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained by history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron’s intrinsic excitability. Decoding single motor unit (MU) spiking activity, that is, action potentials discharged by motor neurons and their innervated muscle fibres, non-invasively from the surface electromyogram (EMG), represents a viable alternative to invasive brain recordings for neural human–machine interfaces . One potnatural functional benefit for the human motor system. Indeed, most previous observations indicate that, contrary to independent control, single MUs tend to be activated in a very stereotyped way which is determined by the common input received by functional groups of MUs  without any known s of MUs  and by tPrevious works have tried to challenge the perspective of single MUs only being activated in a predetermined fashion . A recenThis study examined whether humans could control pairs of MUs innervating the same muscle flexibly. Further, it addressed how this potential ability depends on the similarity of the MU pairs in size or, equivalently, in recruitment threshold. For this purpose, we used a neural interface that provided subjects with biofeedback on the activity of individual MUs . SubjectR = 0.53, p<0.001; see During the main experimental sessions, on average, 11.04 ± 3.34 MUs were reliably decomposed (online surface EMG decomposition) per subject (N = 7). An identified MU pool from a subject sorted based on recruitment order is shown in During the target task, subjects were asked to navigate a cursor inside a 2D space by modulating the discharge rate of MU1 and MU2. Three different targets inside the 2D plane were used to encourage subjects to activate both MUs independently despite their different position within the recruitment order. An entire trial cycle of this target task is illustrated in For example, to reach TIII, subjects must keep the higher threshold unit MU2 active while keeping the lower threshold unit MU1 off. Taken together, target and angle hits, as well as the performance metric, indicate that subjects improved in navigating the cursor towards the target-of-interest across days. However, the main improvement was observed for reaching TIII, which started from poor initial performance. Moreover, subjects experienced a steep learning curve at the beginning of the experiment, while the learning rate seemed to decrease towards the end. For this reason, further analysis only focuses on the last 5 days of training to avoid additionally induced variability by greater learning rates at the beginning of training.R=0.46, p=0.001; see The performance of reaching each target did not solely depend on the target’s position but also on the difference in recruitment and de-recruitment threshold within the selected MU pair, that is, the force difference between the onset and offset of activity between MU2 and MU1 measured during the initial force task . During the last 5 days of training, the majority subjects reported that they felt having control over MU2 when reaching towards TIII  for all 30 trials is visualised in By analysing the unintended target and angle hits see , the prodirect movements towards TI and TIII with respect to the difference in recruitment threshold within the selected pair and subjects is shown in While only a few unintended hits occurred across targets and conditions, unintended angle hits of TII happened multiple times when subjects tried to reach for TIII. In fact, across all conditions, almost all subjects conducted most of their unintended hits when aiming for TIII (with unintended hits in TII). Only one subject had a higher rate of unintended hits when aiming for TII (with unintended hits in TI) in condition I. Therefore, the strategy to reach TIII, that is, moving towards TII first, as illustrated in In these rare cases in which direct movements towards TIII ended, at least, in an angle hit, the level of force and global EMG of tibialis anterior muscle (TA), fibularis longus (FL), lateral head of the gastrocnemius muscle (GL), and medial head of the gastrocnemius muscle (GM) at the time point of recruitment of MU2 was compared with the corresponding values obtained at normal recruitment of MU2 during the initial force ramps . During direct movement towards TIII, MU2 was recruited on average at a 48.43 ± 22.46% lower force level than during ramp recruitment. Also, the global EMG values at recruitment of MU2 were, on average, slightly lower during direct movements towards TIII .However, in the vast majority of cases, target hits of TIII were not achieved by direct movements towards the target centre. Instead, subjects used a three-stage approach to place the cursor inside TIII, as observed in The force, global TA, FL, GL, and GM EMG values in 100 ms window centred around the onset of MU activity were compared before and after the experiment to investigate potential changes in the recruitment order due to single MU modulation. A subtle decrease in force  and global TA EMG (–2.35 ± 22.62%) were identified. However, the global EMG from the lower leg muscles not used for the MU decomposition increased slightly . These changes indicate that the overall recruitment order did not change critically due to the imposed single MU modulation. The increase in activity in the lower leg muscles not directly involved in ankle dorsiflexion relative to the agonist muscle might be explained by induced fatigue towards the end of the experiment .Surface EMG decomposition with methods similar to those used in this study has been previously extensively validated, including by comparison with intramuscular EMG decomposition . HoweverThe analysis of the other contractions recorded for validation led to results similar to those presented representatively in From the validation tests, we concluded that the online surface EMG decomposition in variable force contractions with recruitment and de-recruitment achieved similar performance as the decomposition in static contractions.Volitional and flexible control of single MUs could revolutionise neural-interface applications. Here, we used real-time biofeedback on single MU activity to encourage subjects to learn independent control of pairs of MUs. Our results showed that subjects could gain control over four MUs from a single muscle. The control strategies that emerged, allowing for selective MU control, were limited by the presence of a common input to the MU pool. Therefore, subjects did not exploit potential neural structures with selective inputs to individual MUs.In this study, the identified MU pools were ranked based on their recruitment order. We have shown that the de-recruitment threshold does not always match the recruitment one. While small deviations between recruitment and de-recruitment threshold may be explained by a slight increase in co-contraction during the declining force ramp , larger An inhibitory input is needed to extinguish the impact of PICs on the MU discharge behaviour . Such anPICs act diffuse across the entire MU pool . TherefoDuring a progressive increase in force, recruitment depends only on the MU anatomy and the input received. If humans can learn to leverage potential structures in the CNS that allow selective inputs to MUs , changesgeneral flexible control of MUs, that is, volitional activation of higher threshold MUs before lower threshold ones at initial recruitment. However, flexible control of individual MUs could still be a framework explaining how subjects were able to reach the different targets in the 2D space. If this was the case then, since control would be achieved only after MUs were recruited, this would imply that flexible control is state-dependent: it can only be achieved in the context of previously contracted muscle fibres. Such state dependency restricts the possible neural strategies that could allow flexible control of MUs. One possible strategy consistent with such state-dependent control of individual MUs could be relying on an input signal to MUs not directly linked to motor function and non-homogeneously distributed among the MU pool. For example, cortical oscillations could meet these criteria if descending projections of this activity to large and small MUs in a pool differed .A recent study in humans provided evidence for the existence of MU pool synergies similar to the functional grouping of muscles involved in a single movement . The CNSPrevious studies investigating flexible MU control focused on upper-limb muscles . Here, wThroughout the experiment, subjects were restricted by an ankle dynamometer and instructed to perform dorsiflexion of the ankle only. We measured force due to dorsiflexion but not, for example, rotational forces due to ankle supination. Modest rotations of the ankle can increase the effect of inhibition in specific MUs and thus might affect their excitability . Such efHenneman’s size principle and a common input to their motor neurons. Conversely, the observed strategies do not leverage potential pathways that may provide selective inputs to single MUs. It is concluded that converting common input to a (synergistic) pool of motor neurons into independent input to single MUs within the same task seems extremely challenging for the CNS.To summarise, we have demonstrated the ability to control up to four MUs from a single muscle using real-time feedback on single MU discharge behaviour. Furthermore, we have shown by operant conditioning that subjects learn concrete control strategies to recruit and de-recruit several MUs volitionally. These strategies exploit orderly recruitment in agreement with the Seven healthy subjects  were recruited for the study of whom three are authors of this article. Four subjects were naïve to the experimental paradigm, while the remaining three were recently exposed to single MU feedback. Experiments were carried out over 14 days in blocks of 4–5 consecutive days with never more than 2 days of break between blocks. Each experimental session lasted approximately 2 hr. One subject withdrew from the experiment after only 10 sessions due to time constraints. The study was approved by the ethics committee at Imperial College London (reference number: 18IC4685).High-density surface electromyogram (HDsEMG) was acquired from the TA of the dominant leg via a 64-electrode grid . The adhesive electrode grid was placed over the muscle belly aligned to the fibre direction. In addition, EMG from the FL, GL, and GM were recorded throughout the experiment via pairs of wet gel electrodes  placed over the muscle belly. All EMG signals were monopolar recorded, amplified via a Quattrocento Amplifier system (OT Bioelettronica), sampled at 2048 Hz, A/D converted to 16 bits, and digitally band-pass filtered (10–500 Hz). The foot of the dominant leg was locked into position to allow dorsiflexion of the ankle only. The force due to ankle dorsiflexion (single degree of freedom) was recorded via a CCT TF-022 force transducer, amplified (OT Bioelettronica), and low-pass filtered at 33 Hz. The communication between the amplifier and the computer was conducted via buffers, that is, data packages of 256 samples corresponding to a signal length of 125 ms. All incoming EMG signals were band-pass filtered between 20 and 500 Hz using a fourth-order Butterworth filter. Bipolar derivations were extracted from the filtered EMG signals obtained from the FL, GL, and GM.Subjects were instructed to perform maximum isometric dorsiflexion of the ankle to estimate the MVC level. The obtained MVC was then set as a reference value for the subsequent experimental session. In a sub-MVC task, subjects were instructed to follow a 4 s ramp trajectory (2.5% MVC/s) followed by a constant phase at 10% MVC of 40 s. In both tasks, visual feedback of the force produced by isometric TA contractions was provided. Based on the EMG of the TA recorded during this sub-MVC task, the separation matrix used by an online decomposition algorithm was generated to extract MU discharge behaviour in real time  followed by a 10 s plateau at 10% MVC and a 10 s ramp decline (–1% MVC/s) guided by visual feedback of the force. This ramp trajectory was repeated five times with 5 s rest period in between ramps. Only the first ramp was used for further analysis since repeated activation of MUs within a short time may influence the recruitment order . If a suMU1, while the MU recruited last as MU2. The selected MUs and their decoded spiking activity are exemplarily shown in Subjects were provided with visual feedback on the ranked MU activity. In an exploration phase (approximately 10 min), subjects were instructed to recruit MU one by one by gradually increasing the contraction level of the TA until all identified MUs were discharging action potentials. The entire pool was divided into two sub-pools comprising the first and last recruited half of MUs, respectively. One pair of MUs with a similar recruitment threshold from each sub-pool was randomly selected. Hereby, MU pairs were excluded from the selection if subjects could not recruit these two MUs one by one even after the initial exploration phase. The MU recruited first in each pair was labelled as In the main experiment, subjects navigated a cursor in a 2D space by modulating the discharge rate of MU1 and MU2. The normalised discharge rate of these two MUs was used to span the manifold with units ranging from 0 to 1 along both axes. This target space included three targets of equal size  placed along the axes  and the diagonal . In addition, each target was framed by an angle space comprised of a triangle with one corner in the coordinate origin  and two sides to be tangent at the circumcircle of the corresponding target. Towards the coordinate origin, the angle area was cropped by a circle centred at the origin with a radius of 40% of the normalised discharge rate. In order to navigate the cursor inside this angle space, subjects would need to generate the same discharge relationship between MU1 and MU2 as for reaching towards the target area but without necessarily matching the exact discharge rate determined during 10% MVC. For example, to place and hold the cursor inside the angle space of TI, subjects would need to keep MU1 active while MU2 was inactive. However, the discharge rate of MU1 could be different from its discharge rate at 10% MVC required to place the cursor on the centre of TI. This means the cursor could be placed at , resulting in an angle hit but not a target hit.The discharge behaviour of MU1 and MU2 was decomposed from the acquired HDsEMG in real time. The obtained discharge rates were averaged over the preceding eight buffers (corresponding to 1 s) and normalised by the average discharge rate at 10% MVC of the respective MUs . The cursor movement was updated every buffer (corresponding to 125 ms) and smoothed over six buffers (corresponding to 750 ms) using a moving average. In total, the moving average on the discharge rates and the cursor position resulted in a weighted average on the discharge behaviour of MU1 and MU2 over an effective window of 1625 ms. However, the visual feedback was updated every 125 ms. Because of the update rate, as soon as the discharge behaviour of the selected MU changed, subjects were aware of the change due to the visual display of MU discharges and the resulting cursor movement. In an initial familiarisation phase, subjects could freely move inside the target space and explore different control strategies. During this period, the gain along each axis was set manually to enable subjects to reach the target areas without overexerting themselves to prevent symptoms of muscle fatigue. On average, across all subjects, the gain was increased to 1.15 ± 0.01 for MU1 and 1.16 ± 0.01 for MU2, respectively.After familiarisation with the target environment, subjects were asked to rest at the coordinate origin. Once the target-of-interest appeared, indicated in blue, a trial started, and subjects were instructed to navigate the cursor as quickly and as directly as possible into the target area. If the cursor was kept inside the target-of-interest for at least seven consecutive buffers , subjects were granted a target hit, and the trial ended. The trial was also terminated if subjects failed in navigating and holding the cursor inside the target area within 20 s. If the subjects kept the cursor inside the angle area of the target-of-interest for seven consecutive buffers before the trial was terminated, the subjects were granted an angle hit. Therefore, in a single trial, subjects could achieve both an angle and target hit. A target hit was indicated via colour change of the target-of-interest to green, an angle hit to yellow, and a failure (no target nor angle hit within 20 s) to black. Once the trial ended either after 20 s or a target hit, the subject was instructed to navigate the cursor back into the coordinate origin and rest there for at least 2 s. The entire trial cycle is illustrated in During the familiarisation phase and experiment, subjects received both feedback on the cursor movement and the underlying discharge behaviour of the selected MU pair updated for every buffer of incoming data, that is, 125 ms. The EMGs of the TA, FL, GL, and GM acquired during both force tasks before and after the target task were rectified and low-pass filtered at 10 Hz with a fourth-order Butterworth filter. Similar to the estimation of the force level at the on- and offset of MU activity , the average global EMG values to MU de-/recruitment of all muscles were calculated. The average value inside a 100 ms window centred around the time point of recruitment and de-recruitment for each MU and muscle was calculated across all ramps. This was separately repeated for all values acquired before and after the target task. Three subjects did not repeat the force task after the target task and only followed a single ramp at the beginning of the experiment. Moreover, no de-recruitment threshold was determined for those subjects.If subjects failed to navigate and hold the cursor for 875 ms inside the target-of-interest within the 20 s time window, the nearest miss was calculated. The nearest miss was defined as the average cursor position over 875 ms with the shortest Euclidean distance towards the centre of the target-of-interest. In addition, as previously described in TI and TIII required the sole activation of either MU1 or MU2. To assess subjects' performance in navigating the cursor towards these two targets even when neither the target nor the angle area was reached, but, for example, the discharge rate of the target MU was greater than the one of the other, a new performance metric was introduced that compensates shortcomings of traditional metrics, for example, time-to-target and distance-to-target. This new performance metric was designed to endorse scenarios in which this performance metric was defined asperformance values between 0 and 1, the obtained result was normalised with the worst and best performance values estimated per target. The best performance value for each target across subjects was obtained by simulating cursor movement based on artificially generated MU discharge patterns that match the required activation of MU1 and MU2 to hit the respective target. For example, the best performance for TI was estimated based on discharge behaviour for MU1 that matched a normalised discharge rate equal to 1 and MU2 equals 0. The performance value during the idealised cursor movement until the target hit was used as the corresponding best performance value. For the worst performance value, the performance was calculated as if the cursor was kept in the origin for the entire 20 s.where The described metrics were calculated across all conditions and subjects. Three subjects started with condition III only from day 10 onwards.After every condition, subjects were provided with a questionnaire. Subjects were asked to indicate the level of control they had over MU1 when reaching towards TI, over MU2 when going towards TIII, and both MUs when reaching towards TII. Moreover, whether subjects felt they were using a specific strategy when going to the selected target-of-interest and how cognitively demanding it was to control the MUs together and independently was assessed. When applicable, they were asked to explain their strategy. Three subjects did not fill out the questionnaire.A validation study was performed on three subjects  to compare the on- and offset activity of MUs detected via surface and intramuscular EMG recordings. For this purpose, surface EMG signals were recorded in the same way as in the main experimental task by a 64-channel high-density grid placed over the TA muscle and aligned to the fibre direction of the dominant leg. In addition to the non-invasive grid, two fine-wire electrodes were inserted with an insertion angle of 45° to a depth of a few millimetres below fascia, 10 mm proximal with respect to the top of the electrode grid. The foot was locked into position to allow for the ankle dorsiflexion only, as for the main experimental session. All signals were acquired synchronously with a Quattrocento Amplifier system (OT Bioelettronica), sampled at 10,240 Hz, A/D converted to 16 bits, and digitally band-pass filtered (10–4400 Hz).For the validation test, the participants were provided with visual feedback of the ankle dorsiflexion force and were presented with six target force profiles. All force profiles consisted of a ramp-up trajectory, a 20 s constant force phase at 10% MVC, and a ramp-down trajectory. The ramp-up and ramp-down phases had a slope of 0.5% MVC/s, 1% MVC/s, 2% MVC/s, 2.5% MVC/s, 5% MVC/s, and 10% MVC/s. Each target was repeated twice. In this way, the contractions included recruitment and de-recruitment for a broad range of contractions speeds.The HDsEMG signals were real-time decomposed during the contractions in the same way as done in the main experimental session. The intramuscular EMG signals concurrently recorded were analysed offline and decomposed with the EMGLab software (setting a high-pass filtering at 1000 Hz) . The surStatistical analysis was conducted using SPSS  and MATLAB  for the linear mixed model analysis. The threshold for statistical significance was set to p<0.05. To avoid accumulation of type I errors, non-parametric tests were used to assess the relationship between variables . To comp The present study indicates that humans cannot easily learn to control multiple motor units innervating a single muscle independently. These results suggest that common drive to motor units and the size-recruitment principle impose strong constraints on the motor system and, as such, on the use of high-resolution muscle recordings as a means of controlling brain-machine interfaces. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 4 peer reviewers, and the evaluation has been overseen by Andrew Pruszynski as the Reviewing Editor and Tirin Moore as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Andrew Fuglevand (Reviewer #1).Thank you for submitting your article "The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The paper needs to provide clear demonstration/analysis of the spike sorting algorithm specifically showing that it can accurately differentiate the onset and offset times of pairs motor units selected from Group 1 and pairs selected from Group 2. This can be done based on the authors already-published simultaneous surface/intramuscular recordings but it should also include an analysis/estimate of accuracy when the method is applied to the data in the present paper.2) Given the smoothing of firing rates being used, the authors need to demonstrate that the participants could have, in principle, used the visual feedback to discriminate recruitment times of motor unit pairs. One way of doing this is generating a series of videos showing the visual feedback that would have been shown to a participant in which two motor units are recruited at different times relative to one another (this would be synthetic data). That is, artificially shift the timing of an MU1 spike train to the right for a Condition III trial - a few videos with shift increments of ~250 ms seem reasonable for such an illustration.3) The authors suggest PICs as the explanation of selective de-recruitment of lower threshold units. As described by Reviewer #2, inhibitory inputs will also be shaped by the size principle such that low threshold (i.e. high input resistance) neurons will exhibit greater hyperpolarization. This could lead to a situations where lower threshold neurons become deactivated before higher threshold ones. Please consider this and other explanations in the revised Discussion.4) The authors should directly discuss the differences between upper and lower limbs in terms of control and thus potential deviations the size-recruitment principle.Reviewer #1 (Recommendations for the authors):1.  "These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy" The last phrase "is not a simple to learn control strategy" seems pretty waffly. I would suggest replacing it with something like "is unlikely".2.  "and faster" perhaps substitute "and with higher rates".3. In his Handbook of Physiology Chapter (Henneman & Mendell (1981) Functional organization of motoneuron pool and its inputs) Henneman describes attempts to alter recruitment order with biofeedback that would seem relevant to the present manuscript:"In six of the nine subjects no changes in recruitment order were observed despite two hours of training and the help of audiovisual feedback. In each experiment recordings were made from many sites, and the subject was encouraged to explore maneuvers that might lead to alteration in recruitment. At each new site at least 20-30 minutes was spent attempting to alter the normal order. In not a single instance, out of hundreds of trials, was anyone of these six subjects able to recruit two units in their usual small-to-Iarge order and then turn off unit 1 without silencing unit 2"."The results at almost all recording sites in the three remaining subjects were similar to those just described. In each of these subjects, however, there was one site at which some variability in recruitment order was observed. Although one unit was recruited first and dropped out last in the majority of tests, the unit that was usually recruited second was occasionally the first to respond and could then be activated repetitively for some seconds without any activity in the first unit. These changes in recruitment order seemed to occur randomly. None of the subjects could, on demand, activate unit 2 at will or alternate the activity of the two units in sequence."4.  "and appears to remain robust in various scenarios [21], [22]". There would seem to be other citations, perhaps even more relevant than [21],[22], that might be cited here. These include:• Desmedt JE & Godaux E (1977). Ballistic contractions in man: characteristic recruitment pattern of single motor units of the tibialis anterior muscle. The Journal of Physiology 264, 673-693.• Thomas JS, Schmidt EM & Hambrecht FT (1978). Facility of motor unit control during tasks defined directly in terms of unit behaviors. Experimental Neurology 59, 384-397• Thomas CK, Ross BH & Stein RB (1986). Motor-unit recruitment in human first dorsal interosseous muscle for static contractions in three different directions. Journal of Neurophysiology 55, 1017-1029• Thomas CK, Ross BH & Calancie B (1987). Human motor-unit recruitment during isometric contractions and repeated dynamic movements. Journal of Neurophysiology 57, 311-324.• Jones KE, Lyons M, Bawa P & Lemon RN (1994). Recruitment order of motoneurons during functional tasks. Exp Brain Res 100, 503-5085.  "This indicates that subjects experienced difficulties in keeping MU2 active while MU1 is inactive in order to reach TIII when their difference in recruitment threshold was large". "Large" is a relative term. Indeed, the actual difference in recruitment thresholds was quite small, on the order of only 6 – 10 % MVC. Perhaps instead state something like "when their difference in recruitment threshold was relatively large (6 – 10 % MVC)."6. (pg 10 ln 27 ) [This is a minor point and needs to be addressed only if the authors wish to] "in 64.73% a selective MU was de-recruited at a force level below its initial recruitment threshold". One likely explanation for this is that during the decrease in force phase, subjects slightly increased activity of the antagonist muscles (De Luca CJ & Mambrito B (1987). Voluntary control of motor units in human antagonist muscles: coactivation and reciprocal activation. J Neurophysiol 58, 525-542). Even a modest degree of antagonist activity would cause the net (measured) force at derecruitment of a MU to be somewhat less, even though the muscle (TA) force might still be the same as at recruitment (Patten C & Kamen G (2000). Adaptations in motor unit discharge activity with force control training in young and older human adults. Eur J Appl Physiol 83, 128-143; Fuglevand AJ, Dutoit AP, Johns RK & Keen DA (2006). Evaluation of plateau-potential-mediated "warm up" in human motor units. The Journal of Physiology 571, 683-693)7. (pg 19 ln 4) "An inhibitory input is needed to extinguish the impact of PICs on the MU discharge behaviour." A citation should probably be included here.8.  "but also other motor behavioural changes, including alternations in postures [36], and contraction speed [15], are well known factors that impact the recruitment order." However, plenty of other studies suggest that these factors have little clear-cut effect on recruitment order (see citations under point 4 above).9.  "changes in a MU pool's discharge activity imposed by such behavioural changes were recently confirmed". The word "confirmed" is far too strong. The paper cited has not undergone peer review. Moreover, the results of that manuscript are questionable for several technical reasons. Suggest change "confirmed" to "suggested".10.  Paragraph beginning "A recent study in humans provided evidence for the existence of MU pool synergies" seems somewhat ancillary to the main topic of this paper and could be eliminated.Reviewer #2 (Recommendations for the authors):1. Because the findings depend on the reliable isolation of single MUs, the authors should provide additional characterization of this process. (A) Please show a segment of raw data  with the spike times for selected MUs highlighted. (B) Provide more details on quality metrics  or criteria . A sensitivity analysis to determine whether the main results are robust to more stringent isolation criteria might also be useful.2. Ideally, readers should be able to interpret the figures with minimal reference to the text or caption. To this end, additional annotation in Figure 3A and 4 indicating that the y-axis corresponds to the higher-threshold unit would be useful. Similarly, in Figure 5A, please provide additional annotation for Conditions I-III , as well as labels for the Y axes of the confusion matrices . I would also recommend a uniform color scheme for the entries in the confusion matrices in Figure 5A , to allow an easy graphical comparison across rows, columns, and conditions.3. Please show a scatterplot with the TIII hit rates on TIII-instructed trials vs threshold difference for all pairs.4. A more explicit description of the decoding scheme would help. It appears that x-y cursor positions are weighted averages of MU1 / MU2 discharge rates over the previous second; this could be stated more directly, and additional filtering procedures, if any, described.5. Figure 1: the "green arrows" indicating the electrodes in the caption do not appear to be in the panel.Reviewer #3 (Recommendations for the authors):NoneReviewer #4 (Recommendations for the authors):eLife are aimed toward a broad readership, the writing could be clearer at certain points. For example:1. Since papers in a. The acronym DR is never spelled out.b. The term buffer is confusing.c. Some statements were a bit vague. For example, page 3, line 12-14; page 7, line 18-19; page 21, line 27-28eLife. Your revised article has been evaluated by Tirin Moore (Senior Editor) and Andrew Pruszynski (Reviewing Editor).Thank you for resubmitting your work entitled "The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal" for further consideration by The manuscript has been substantially improved but there are some remaining issues that need to be addressed, as described below:1. There remains some concern about the validation (required revision #1 in the previous review). First, it would be good to show in a scatter plot the agreement fraction during recruitment vs. derecruitment as calculated for the common MUs . Such a plot could highlight any consistent bias between the two epochs. Second, it is unclear how you decide on common MUs. Is this decision related in some way to the agreement fraction? If so, this appears to be a bit of a circular argument. Please explain why it is not circular in the manuscript.2. The title needs to be modified to accurately reflect the main findings of the study. Specifically, please remove the word synaptic as the precise mechanism is ultimately unknown in recruitment and de-recruitment. One possibility might be: "The recruitment of single motor units in a trained isometric task is constrained by a common input signal"3. Similar to above, the end of the abstract should also be modified to ensure it accurately reflects the bounds of the paper. The last three sentences are presently:"These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is unlikely."Suggestion to clarify: First sentence: "… independent input signals in individual MUs during recruitment/onset of muscle activity". Last sentence: "These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect either varying inputs or PIC" Essential revisions:1) The paper needs to provide clear demonstration/analysis of the spike sorting algorithm specifically showing that it can accurately differentiate the onset and offset times of pairs motor units selected from Group 1 and pairs selected from Group 2. This can be done based on the authors already-published simultaneous surface/intramuscular recordings but it should also include an analysis/estimate of accuracy when the method is applied to the data in the present paper.We have decided to perform a new experiment specifically for testing accuracy in the new conditions of this study. As indicated above, the developed algorithm for online decomposition has been extensively validated in stable force contractions without recruitment/de-recruitment. We have therefore added new concurrent intramuscular and surface EMG data in conditions of recruitment/derecruitment and have looked at the rate of agreement between the decompositions from the two types of signals. We underline here that this is the best possible validation for a surface EMG decomposition algorithm, as we discussed previously. The new experiment and results are described in the revised manuscript . Briefly, the new experimental protocol was performed on three subjects and included online surface EMG decomposition in the same way as done in the main experimental session. In these new measures, however, intramuscular signals (wire electrodes) were concurrently recorded. Since the focus was to prove that the online decomposition could track recruitment and de-recruitment with high accuracy, the subjects were asked to follow force profiles of increasing and decreasing force at six different speeds. This allowed to look at recruitment/de-recruitment in a variety of conditions, from slow to fast . Each force trajectory and force speed was repeated twice. Thus, in total we recorded data from 36 contractions , with concurrent data from surface and intramuscular electrodes. The surface signals were decomposed online during the contractions, in the same way as in the main experimental session, and the decomposition result was stored for further analysis. The intramuscular signals were decomposed offline by a semi-automatic procedure, with a validated and extensively used intramuscular EMG decomposition algorithm . The two decompositions were assessed with the rate of agreement, that is the percentage of discharge instants detected by both decompositions within a 1-ms delay margin. This is the same metrics for validation we previously used for analysing stable force contractions and it is a conservative metrics of accuracy . The results were very clear and indicated (1) that the overall accuracy was comparable to those observed for stable force contractions in previous validation studies , and (2) that the errors were distributed uniformly during the contraction, without any preferential distribution around the time of recruitment of derecruitment. Notably, we never observed an error in the first discharge (onset) or the last discharge (offset), meaning that the recruitment and derecruitment of motor units was detected by the online decomposition with an accuracy of 1ms . These results strongly support the conclusion that the online surface EMG decomposition used in this study during variable force contractions with recruitment and de-recruitment of motor units achieved similar (very highly accurate) performance as the decomposition in static contractions. We are particularly satisfied of this added validation since it can serve as a reference for future studies performed in similar conditions.The revised manuscript includes all the above information in mode details, as well as a representative figure of the validation procedure and numerical results.2) Given the smoothing of firing rates being used, the authors need to demonstrate that the participants could have, in principle, used the visual feedback to discriminate recruitment times of motor unit pairs. One way of doing this is generating a series of videos showing the visual feedback that would have been shown to a participant in which two motor units are recruited at different times relative to one another (this would be synthetic data). That is, artificially shift the timing of an MU1 spike train to the right for a Condition III trial -- a few videos with shift increments of ~250 ms seem reasonable for such an illustration.We have added a video  showing the biofeedback environment presented to the subjects with real data from a TII-instructed trial. The trial was repeated nine times, with MU1 being artificially delayed each iteration by 250ms. The video layout shows the feedback subjects have received, i.e. cursor movement and the underlying neural activity of the selected MU pair. Indication of current force level, target-of-interest, angle or target hits were neglected in this simulation. In addition, we clarified throughout the manuscript that subjects were provided with realtime feedback on both the discharge activity of the selected MU pair and the resulting cursor movement.3) The authors suggest PICs as the explanation of selective de-recruitment of lower threshold units. As described by Reviewer #2, inhibitory inputs will also be shaped by the size principle such that low threshold (i.e. high input resistance) neurons will exhibit greater hyperpolarization. This could lead to a situations where lower threshold neurons become deactivated before higher threshold ones. Please consider this and other explanations in the revised Discussion.We agree with the reviewer’s comment and thus updated the Discussion accordingly.4) The authors should directly discuss the differences between upper and lower limbs in terms of control and thus potential deviations the size-recruitment principle.Following the reviewer's comment, we have included a new paragraph addressing the point about potential differences between upper- and lower-limbs and justifying the use of a lower leg muscle in our work at p. 22, l. 16-29.Reviewer #1 (Recommendations for the authors):1.  "These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy" The last phrase "is not a simple to learn control strategy" seems pretty waffly. I would suggest replacing it with something like "is unlikely".We changed the last phrase of the abstract accordingly.2.  "and faster" perhaps substitute "and with higher rates".Thank you for this comment; we have adapted the manuscript accordingly.3. In his Handbook of Physiology Chapter (Henneman & Mendell (1981) Functional organization of motoneuron pool and its inputs) Henneman describes attempts to alter recruitment order with biofeedback that would seem relevant to the present manuscript:"In six of the nine subjects no changes in recruitment order were observed despite two hours of training and the help of audiovisual feedback. In each experiment recordings were made from many sites, and the subject was encouraged to explore maneuvers that might lead to alteration in recruitment. At each new site at least 20-30 minutes was spent attempting to alter the normal order. In not a single instance, out of hundreds of trials, was anyone of these six subjects able to recruit two units in their usual small-to-Iarge order and then turn off unit 1 without silencing unit 2"."The results at almost all recording sites in the three remaining subjects were similar to those just described. In each of these subjects, however, there was one site at which some variability in recruitment order was observed. Although one unit was recruited fIrst and dropped out last in the majority of tests, the unit that was usually recruited second was occasionally the fIrst to respond and could then be activated repetitively for some seconds without any activity in the first unit. These changes in recruitment order seemed to occur randomly. None of the subjects could, on demand, activate unit 2 at will or alternate the activity of the two units in sequence."Thank you for pointing this out. We have included the reference at p. 12, l. 14-15.4.  "and appears to remain robust in various scenarios [21], [22]". There would seem to be other citations, perhaps even more relevant than [21],[22], that might be cited here. These include:• Desmedt JE & Godaux E (1977). Ballistic contractions in man: characteristic recruitment pattern of single motor units of the tibialis anterior muscle. The Journal of Physiology 264, 673-693.• Thomas JS, Schmidt EM & Hambrecht FT (1978). Facility of motor unit control during tasks defined directly in terms of unit behaviors. Experimental Neurology 59, 384-397• Thomas CK, Ross BH & Stein RB (1986). Motor-unit recruitment in human first dorsal interosseous muscle for static contractions in three different directions. Journal of Neurophysiology 55, 1017-1029• Thomas CK, Ross BH & Calancie B (1987). Human motor-unit recruitment during isometric contractions and repeated dynamic movements. Journal of Neurophysiology 57, 311-324.• Jones KE, Lyons M, Bawa P & Lemon RN (1994). Recruitment order of motoneurons during functional tasks. Exp Brain Res 100, 503-508We agree that these references add further substance to this claim and thus added them to the manuscript.5.  "This indicates that subjects experienced difficulties in keeping MU2 active while MU1 is inactive in order to reach TIII when their difference in recruitment threshold was large". "Large" is a relative term. Indeed, the actual difference in recruitment thresholds was quite small, on the order of only 6 – 10 % MVC. Perhaps instead state something like "when their difference in recruitment threshold was relatively large (6 – 10 % MVC)."We followed the reviewer’s suggestion.6. (pg 10 ln 27 ) [This is a minor point and needs to be addressed only if the authors wish to] "in 64.73% a selective MU was de-recruited at a force level below its initial recruitment threshold". One likely explanation for this is that during the decrease in force phase, subjects slightly increased activity of the antagonist muscles (De Luca CJ & Mambrito B (1987). Voluntary control of motor units in human antagonist muscles: coactivation and reciprocal activation. J Neurophysiol 58, 525-542). Even a modest degree of antagonist activity would cause the net (measured) force at derecruitment of a MU to be somewhat less, even though the muscle (TA) force might still be the same as at recruitment (Patten C & Kamen G (2000). Adaptations in motor unit discharge activity with force control training in young and older human adults. Eur J Appl Physiol 83, 128-143; Fuglevand AJ, Dutoit AP, Johns RK & Keen DA (2006). Evaluation of plateau-potential-mediated "warm up" in human motor units. The Journal of Physiology 571, 683-693)We have added this concept to the Discussion at p. 18, l. 9-11.7. (pg 19 ln 4) "An inhibitory input is needed to extinguish the impact of PICs on the MU discharge behaviour." A citation should probably be included here.We have added reference [31] to this sentence.8.  "but also other motor behavioural changes, including alternations in postures [36], and contraction speed [15], are well known factors that impact the recruitment order." However, plenty of other studies suggest that these factors have little clear-cut effect on recruitment order (see citations under point 4 above).We have toned down our claim at p. 21, l. 15.9.  "changes in a MU pool's discharge activity imposed by such behavioural changes were recently confirmed". The word "confirmed" is far too strong. The paper cited has not undergone peer review. Moreover, the results of that manuscript are questionable for several technical reasons. Suggest change "confirmed" to "suggested".We have modified the manuscript accordingly.10.  Paragraph beginning "A recent study in humans provided evidence for the existence of MU pool synergies" seems somewhat ancillary to the main topic of this paper and could be eliminated.This paragraph helps clarifying the importance to constrain the functional output of a single anatomical unit, i.e. a muscle, when flexible MU control based on individual descending commands is studied. Recent investigations have shown that a single anatomical unit can receive multiple common inputs to sub-pools of MUs. Thus, flexible MU control within this single anatomical unit can be triggered by changing the motor task the muscle is involved in. Therefore, this paragraph may help the consideration and design of future studies aiming to investigate flexible MU control due to selective descending inputs.Reviewer #2 (Recommendations for the authors):1. Because the findings depend on the reliable isolation of single MUs, the authors should provide additional characterization of this process. (A) Please show a segment of raw data  with the spike times for selected MUs highlighted. (B) Provide more details on quality metrics  or criteria . A sensitivity analysis to determine whether the main results are robust to more stringent isolation criteria might also be useful.We have added Figure 1 – —figure supplement 1  to address the reviewer’s first request. Moreover, we have added a very careful validation of the decomposition with new measures done with concomitant surface and intramuscular recordings during recruitment/de-recruitment of motor units at variable recruitment speed. The details of these new measures and analyses are specified in the reply to the first request for essential changes (see above).2. Ideally, readers should be able to interpret the figures with minimal reference to the text or caption. To this end, additional annotation in Figure 3A and 4 indicating that the y-axis corresponds to the higher-threshold unit would be useful. Similarly, in Figure 5A, please provide additional annotation for Conditions I-III , as well as labels for the Y axes of the confusion matrices . I would also recommend a uniform color scheme for the entries in the confusion matrices in Figure 5A , to allow an easy graphical comparison across rows, columns, and conditions.We have changed all figures following the reviewer’s suggestion.3. Please show a scatterplot with the TIII hit rates on TIII-instructed trials vs threshold difference for all pairs.The reviewer’s suggestion is now included in Figure 3 – —figure supplement 1B .4. A more explicit description of the decoding scheme would help. It appears that x-y cursor positions are weighted averages of MU1 / MU2 discharge rates over the previous second; this could be stated more directly, and additional filtering procedures, if any, described.We have changed the description of the weighted average in the manuscript at p. 26, l. 21-28.In addition, subjects received feedback on the discharge behaviour of the selected MU pair and the corresponding cursor movement. We changed the manuscript at p. 4, l. 6 accordingly to clarify the biofeedback modalities used in the experiment and at p. 27, l. 17-20. To further clarify the decoding of incoming MU1/MU2 discharge rates into cursor movement, we included Video 1 .Reviewer #4 (Recommendations for the authors):1. Since papers in eLife are aimed toward a broad readership, the writing could be clearer at certain points. For example:a. The acronym DR is never spelled out.We spelt out the acronym DR (discharge rate) throughout the manuscript.We have elaborated and clarified all statements.The manuscript has been substantially improved but there are some remaining issues that need to be addressed, as described below:1. There remains some concern about the validation (required revision #1 in the previous review). First, it would be good to show in a scatter plot the agreement fraction during recruitment vs. derecruitment as calculated for the common MUs . Such a plot could highlight any consistent bias between the two epochs. Second, it is unclear how you decide on common MUs. Is this decision related in some way to the agreement fraction? If so, this appears to be a bit of a circular argument. Please explain why it is not circular in the manuscript.Thank you for further concerns on the validation, which we are happy to address.For the first point on validation:We would like to underline the following result reported in the manuscript:“Notably, we never observed an error in the first discharge (onset) or the last discharge (offset), meaning that the recruitment and de-recruitment times were detected by the online decomposition with an accuracy of 1ms ….”Thus, the recruitment and derecruitment thresholds (defined as the instants of first and last activation) for all common motor units were identified with a perfect accuracy according to the time margin adopted in the validation (1 ms). A scatter plot of the time instants for recruitment and derecruitment thresholds for all common motor units would thus simply be the identical line with a variability bounded to 1 ms. This would not be informative since the text already report the needed information.In addition, we already reported the rate of agreement separately for the recruitment phase (ramp up), the stable force phase, and the derecruitment phase (ramp down). Note that the standard deviation for rate of agreement for each of these phases does not exceed 2%. A scatter plot representing the rate of agreement is not possible since when there is no agreement, one spike is missing in one of the two decompositions, and therefore we cannot plot the discharge times in a scatter plot (one of the two coordinates is missing).We hope the above clarifications provide all the further needed information on the first point raised on validation.For the second point on validation:The reviewer is right in that we used the rate of agreement for detecting common motor units. This has been done in previous studies (cited in the manuscript) and consists in putting a relatively low threshold value on the rate of agreement to accept two motor units as commonly detected. In our case, we put a threshold of 30% in rate of agreement. This threshold is so low that does not bias the actual result of validation, as discussed in previous studies. Having said that, in order to fully avoid any doubts on a circular argument, we have now adopted a new criterion for identifying common motor units, fully independent on the rate of agreement. The new implemented procedure is the following. The spike train of each motor unit identified by intramuscular EMG decomposition was used for spiketriggered averaging the multi-channel surface EMG. This provided the estimate of the action potential waveform shape as detected by the surface EMG grid for the motor units identified by intramuscular EMG decomposition. The spike trains of the motor units identified by the surface EMG decomposition were then used for spike-triggered averaging the surface EMG and therefore to obtain the action potential waveform shape at the skin surface for the motor units identified by surface EMG decomposition. The matching for identifying common units was then performed by comparing the shapes of the action potentials at the skin surface obtained from intramuscular and surface EMG decomposition. The comparison was based on a correlation coefficient between action potential waveform shapes >0.9. This approach is fully independent on the rate of agreement. Not surprisingly, when we implemented this approach, we obtained exactly the same 16 common units that we identified by the threshold of 30% on rate of agreement. The results on validation have therefore remained exactly the same. We have now described the new method for selecting common units in the revised manuscript. This new method avoids any doubts on a potential circular reasoning .2. The title needs to be modified to accurately reflect the main findings of the study. Specifically, please remove the word synaptic as the precise mechanism is ultimately unknown in recruitment and de-recruitment. One possibility might be: "The recruitment of single motor units in a trained isometric task is constrained by a common input signal"We have changed the title of the manuscript to “The control and training of single motor units in isometric tasks are constrained by a common input signal” .3. Similar to above, the end of the abstract should also be modified to ensure it accurately reflects the bounds of the paper. The last three sentences are presently:"These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is unlikely."Suggestion to clarify: First sentence: "… independent input signals in individual MUs during recruitment/onset of muscle activity". Last sentence: "These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect either varying inputs or PIC"The last three sentences of the abstract were modified accordingly to suggestion of the editors :“These strategies rarely corresponded to a volitional control of independent input signals to individual MUs during the onset of neural activity. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU recruitment based on independent synaptic inputs to single MUs is unlikely, although de-recruitment might reflect varying inputs or modulations in the neuron's intrinsic excitability.”


Toxicity and efficacy were evaluated. Results: In total, eighteen patients were enrolled: dose level 1 (1000 mg) was assigned to seven patients, dose level 2 (1250 mg), five patients; and dose level 3 (1500 mg), six patients. Dose-limiting toxicities were diarrhea and/or neutropenic enterocolitis observed in two of six patients: one diarrhea III°, one diarrhea IV°, as well as neutropenic enterocolitis. The maximum tolerable dose of lapatinib was 1250 mg OD. Conclusions: The combination of lapatinib 1250 mg OD with platinum-containing chemotherapy is safe and feasible in patients with refractory pancreatic cancer and warrants further investigation.Background: Primary objective of this present trial was to define the maximum tolerable dose of lapatinib in combination with oxaliplatin, 5-fluorouracil, and folinic acid (OFF) in refractory pancreatic cancer. The secondary objective was to assess the safety and efficacy of lapatinib plus OFF. Methods: We conducted a phase I trial using an accelerated dose escalation design in patients with refractory pancreatic cancer. Lapatinib was given on days 1 to 42 in combination with folinic acid 200 mg/m Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor entity expected to become the second leading cause of cancer-related mortality within this decade ,2. TreatCurrently, there are two standard therapies for patients fit to receive a combination therapy for inoperable PDAC: gemcitabine in combination with nab-paclitaxel or the more intensive FOLFIRINOX regimen, which is reserved for fit patients. In case of progression during first-line treatment with gemcitabine +/− nab-paclitaxel, a subsequent treatment usually comprises a fluoropyrimidine-based combination with either oxaliplatin or  irinotecan if an adequate performance status is maintained ,8,9.Taken together, a major challenge in the treatment of pancreatic cancer is the absence of effective targeted substances, as well as the lack of validated predictive biomarkers that might facilitate therapeutic decision-making for the vast majority of patients. Whereas the addition of the EGFR-targeting agent erlotinib to gemcitabine has been demonstrated to slightly improve outcomes compared with gemcitabine monotherapy , the tyrWe conducted a phase I trial to define the maximum tolerated dose of lapatinib in combination with platinum containing chemotherapy (OFF regimen) in PDAC patients pretreated with gemcitabine-based therapy.This open-label, single-center phase I study investigated lapatinib in combination with OFF in patients with PDAC refractory to gemcitabine monotherapy. The primary objective of the trial was the definition of the maximum tolerable dose of lapatinib in combination with OFF; secondary objectives were safety and efficacy. The starting dose of lapatinib was 1000 mg (dose level 1) and subsequently escalated in 250 mg increments. Initiation of part 2 (dose level 2) and part 3 (dose level 3) was performed if none of the three consecutive patients at the respective dose level developed dose-limiting toxicity within the first 42-day cycle of chemotherapy. Observed toxicities were graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events 4.0 (CTCAE). The RECIST (version 1.1) guideline was used for the evaluation of antitumor activity.The trial protocol was approved by the local ethical committee and registered by the European authorities (EudraCT-Nr. 2009-009928-37). The study was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent before any study procedures.Included were patients with histologically proven pancreatic adenocarcinoma and progressive disease during first-line treatment confirmed by CT within the previous 4 weeks. The prerequisite was a normal cardiac function in cardiac ultrasound, normal liver, bone marrow and renal function, a Karnofsky performance status of ≥60 percent, age over 18 years, and written informed consent. The most important exclusion criteria were any history of cardiac arrhythmia, cardiac insufficiency grade NYHA 2 to 4, a history of coronary events, thromboembolic events or cerebral bleeding within the previous 6 months, and prior irradiation. Patients were considered noneligible for statistical analysis if a violation of inclusion criteria was present. The remaining patients were included in the intention-to-treat-analysis of primary and secondary objective analysis. According to protocol analysis, only patients receiving ≥2 cycles of chemotherapy were included (exception: dropout due to early progression/death). In terms of toxicity, all patients receiving at least one cycle of chemotherapy and/or targeted therapy were considered evaluable.This phase I trial was designed as a single-institution, prospective, open-label study of the safety and tolerability of lapatinib administered concurrently to the OFF regimen. Enrolment followed the Simon’s accelerated titration design. Three subjects were accrued in the first stage and completed at least one cycle of study treatment. If none of the three consecutive patients at the respective dose level developed dose-limiting toxicity within the first 42-day cycle of chemotherapy, the dose of lapatinib was escalated. In the case of a DLT, three additional patients were included in the respective dose level. If a DLT occurred in ≥2 patients, the prior dose level was considered as the maximum tolerated dose.2 (30 min) and 5-FU 2000 mg/m2 (24 h continuous infusion) [2 (2 h infusion) was given on days 8 and 22. Lapatinib was applied daily per os with an initial dose of 1000 mg that was planned to be escalated stepwise by 250 mg to a maximum of 1500 mg. Systemic chemotherapy was based on established doses for folinic acid 200 mg/mTo be judged as DLT, a relationship to the study drug was presumed. Therapeutic responses were to be considered confirmed if shown in two subsequent CT scans in a 6-week interval.Response to treatment was evaluated in 6-week intervals by CT scan. For confirmation of response or stable disease, two subsequent routinely performed CT evaluations had to show the respective result. Treatment-related cardiotoxicity was monitored every 6 weeks by cardiac ultrasound and electrocardiogram.The baseline and treatment characteristics of the eligible patients in the trial who received at least one dose of chemotherapy and/or targeted therapy were summarized. All patients lost to study at any stage following registration (including those not receiving the study medication) were reported. The reason for withdrawal was documented and summarized.The MTD and rates of DLTs and tolerability were determined at the end of the dose safety and escalation stage of the study. Rates of DLTs are reported with 95% confidence intervals by the dose level cohort in dose safety and escalation stage (based on the dose received). In order to avoid bias due to the occurrence of DLT with subsequently influencing recruitment, the rates of DLT are also reported for the patients in the expansion cohorts.For the main and final reports after completion of dose escalation, rates of tolerability and grade of adverse events, both reported and only those believed to be possibly, probably, or definitively related to study treatment, were calculated with 95% confidence intervals, overall patients, for those treated at the MTD, and for those treated below the MTD.In total, 18 patients allocated to three different dose cohorts were required to determine the maximum tolerable dose (MTD) of the combination regimen. For baseline characteristics and patient assignment criteria, see Seven patients were included in the lowest dose level. In the therapeutic course of the first three patients, relevant complications occurred. One patient suffered from transitory ischemic neurologic deficit due to carotid arteriosclerosis and interrupted therapy within cycle 1. One patient did not complete the first cycle according to protocol by reason due to hospitalization with hypostatic syncope, diarrhea I°, and hypokalemia III° after vomiting (gastric outlet stenosis); another patient developed fatal liver failure within the first cycle. Initially, it was unknown that the liver failure was a result of severe septicemia  and not possible drug-induced liver toxicity. Thus, we raised the number of patients at the first dose level to acquire sufficient safety data. After receiving all the safety data on the fatal case, it was considered not drug-related but due to sepsis, and further recruitment on this level was stopped. Therefore four instead of three patients were needed to complete this dose level without a DLT. In total, five patients received dose level 2. In one of those patients, treatment was stopped within the first cycle when the patient withdrew his consent for individual reasons. Another patient developed progressive disease with hepatic failure within the first cycle. Both patients were considered not to be evaluable for DLTs because they did not complete the first cycle. Thus, two additional patients had to be recruited. We observed no DLT at this dose level. Six patients were included in the dose level 3 cohort. The cohort was expanded after the first documented DLT. Overall, two patients experienced a DLT . For details on the toxicities during the first cycle, please refer to In order to avoid cumulative toxicities, the three different dose levels were separately given for the subsequent cycles. It has to be considered for interpretation that the number of the given cycles and patients receiving further cycles were necessary. Overall, in dose level 1, 14 subsequent cycles were given  to six remaining patients. For dose level 2, six subsequent cycles  were given to three remaining patients, and for dose level 3, 12 subsequent cycles  were given to four remaining patients. An overview of the given cycles is shown in Thirteen serious adverse events (SAEs) were reported in the course of the trial in nine different patients. Most of the events qualified as SAE through patient hospitalization.One patient experienced a syncope during cycle 1, resulting in hospital admission due to vomiting, dehydration, hypokalemia, and concomitant QT prolongation that improved after fluid and electrolyte substitution. In the same cycle, the patient was again hospitalized with vomiting, hypokalemia, and mild diarrhea as well as QTc time prolongation, which was shown to be caused by gastric outlet stenosis due to local progression of his carcinoma. The data safety board considered that both events were neither related to the study drug nor protocol procedures. One patient suffered from cerebral ischemia due to carotid stenosis within cycle 1 and a traumatic femoral neck shaft fracture during cycle 2, which were both not considered to be related to the study drug or to protocol procedures. One patient developed septicemia, most likely due to cholangitis and simultaneous catheter infection with consecutive thrombocytopenia, leukocytopenia, liver failure and respiratory failure—he died during cycle 1. Blood cultures were positive for lactobacillus and candida glabrata. Thus, an abdominal focus, e.g., cholangitis, seemed probable. The fatal event was considered to be related neither to the study drug nor to protocol procedures.One patient experienced severe hypoglycemia while on insulin therapy in cycles 2 and 4, which were both not considered to be related to the study drug nor protocol procedures.One patient developed gastrointestinal bleeding III° without concomitant thrombocytopenia but concomitant transaminase elevation IV° that was considered to be caused by hypoxic liver damage during cycle 1. The event was due to cancer infiltration of the stomach and thus not judged to be drug-related or to protocol procedures. One patient was hospitalized during cycle 1 for a positive stool test for occult blood; gastroscopy showed no signs of bleeding, and further stool tests were negative. The patient also showed hypokalemia, probably due to pre-existing mild diarrhea. Both events were not considered related to the study drug or to protocol procedures. The same patient was later in the same cycle hospitalized again for cholangitis I°, antibiotics were given and biliary drainage was performed. There was also no relation to the study drug or protocol procedures.One patient developed diarrhea IV° and neutropenic enterocolitis during cycle 1, which was considered related to lapatinib and possibly 5-FU and oxaliplatin and thus considered to be a DLT. One patient was shortly hospitalized in cycle 3 with hyperglycemia and lethargy, most likely due to a new start of parenteral nutrition; both were not considered to be related to the study drug nor protocol procedures. One additional patient developed hyperglycemia during cycle 1 after the start of parenteral nutritional support.In this trial, we found the fixed combination of systemic chemotherapy with oxaliplatin, 5-fluorouracil, and folinic acid combined with a dose of 1250 mg lapatinib daily to be a tolerable combination. Diarrhea III° in one patient and diarrhea IV°, accompanied by febrile enterocolitis in another patient, was defined as a DLT at the dose level of 1500 mg. We consider the dose of 1250 mg as the maximum tolerated dose (MTD) for this combination.2. The MTD was set on lapatinib as 1250 mg daily and capecitabine as 2000 mg/m2.Diarrhea was already the dose-limiting toxicity in the CONKO 003 study establishing the second-line combination with oxaliplatin/5-FU/FS. Doses of lapatinib higher than 1250 mg seem to aggravate this specific side effect. In line with our results, a phase I trial of the combination of lapatinib and capecitabine in patients with solid cancers also observed diarrhea to be the DLT at a combination of lapatinib 1500 mg and capecitabine 2000 mg/mAnother trial evaluating lapatinib and FOLFOX4 in patients with solid cancers found no dose-limiting toxicities up to a lapatinib dose of 1500 mg daily, which is above the MTD found in our trial. However, in the cited trial, DLT was defined differently. In the 1500 mg dose level of that trial, 7 of 28 patients experienced diarrhea III°, but this was only considered to be a DLT in case of maximum supportive care .2 every three weeks together with capecitabine 1500 mg/m2 on days 1–14 and lapatinib in diverse solid cancers, Dennie et al. also found diarrhea as the dose-limiting toxicity, and the daily dosage of 1000 mg lapatinib was the maximum tolerated dose [Evaluating the combination of oxaliplatin 130 mg/mted dose . In our ted dose . InteresIn conclusion, diarrhea seems to be the most prominent toxicity of lapatinib in combination with fluoropyrimidines with or without oxaliplatin, and, in accordance with most other trials, a daily dose of 1250 mg lapatinib was well-tolerated in our trial.Cardiac toxicity is a side effect of many tyrosine kinases and a common side effect of the erbB-2 targeting antibody trastuzumab. In the small number of analyzed patients, one patient showed a transient decrease in ejection fraction, possibly related to lapatinib in cycle 3 of dose level 1, which recovered after stopping the study drug. In this patient, study participation was ended due to progressive disease at the same time. Lapatinib is, as trastuzumab, known to provoke mostly transient decreases in ejection fraction , althougIn the gemcitabine/erlotinib trial, one of the major findings was the prediction of response probability based on rash. The pathophysiology of rash is not yet understood in detail, but delayed maturing of keratinocytes and thinning of superficial skin layers, immune response, and possibly individual differences in response to EGFR inhibition by PI3 kinase activation are discussed . InteresNo higher responses than stabilization were documented in this trial. However, a median OS of 7.6 months after the progression of first line treatment seems to be at least equivalent to the published survival data of the available Phase III second-line trials .The most prominent toxicity of lapatinib in combination with fluoropyrimidines with or without oxaliplatin is gastrointestinal toxicity: diarrhea. Our trial showed that the combination of lapatinib 1250 mg OD with platinum-containing chemotherapy is safe and feasible in patients with refractory pancreatic cancer and warrants further investigation.


We read with interest the comment by Kremsner and Krishna on our paper published on this Journal. The comment correctly points out that we documented an adequate clinical and parasitological response (ACPR) of 44% with quinine plus clindamycin compared with 97% on artemether-lumefantrine assessed on day 28 after starting treatment. We recognize the long-standing research interest by Kremsner and Krishna on the role of clindamycin-based combinations in the treatment of falciparum malaria, demonstrated by their publications. In this comment, Kremsner and Krishna are concerned with the level of ACPR on the quinine plus clindamycin arm in our study. They cite a few of their studies conducted in Gabon and Brazil that evaluated the efficacy of a 3-day course of 12-hourly quinine plus clindamycin treatment for participants with either uncomplicated or severe malaria. In the cited studies, administration of a relatively higher dose of quinine (15 mg/kg bd) combined with a relatively lower dose of clindamycin (7 mg/kg bd) was consistently associated with cure rates of between 88 and 100% (excluding reinfections). They correctly observe that 54% of children treated with quinine plus clindamycin in our study were still parasitaemic on day 3. However, they confirm that the mean parasite clearance with quinine plus clindamycin was slow (48 to 65 h) even in their previous studies and they commonly observed that up to 50% of participants were still parasitaemic on day 3 but “without the need for rescue treatment”. In conclusion, Kremsner and Krishna highlight the continuing need to evaluate non-artemisinin-based combinations for the treatment of uncomplicated malaria using appropriate study designs and correct dosages to avoid exposing participants to undertreatment and possible misinterpretation of the findings.We do not agree with the statement by Kremsner and Krishna that our study was probably not well-designed, which they have not elaborated. We followed the standard methods for the design of randomized controlled parallel-group clinical trials for assessing the treatment efficacy of anti-malarials –6. SpeciOverall, all the studies cited by Kremsner and Krishna had methodological flaws, had used a 3-day 12 hourly regimen of quinine plus clindamycin with comparable doses, long mean parasite clearance times but higher cure rates. It is important to note that there were probably spatio-temporal differences in parasite resistance patterns between Gabon and western Kenya. Only one of the cited studies was closely comparable to our study because they used a well-designed study to compare the efficacy of quinine plus clindamycin to an artemisinin-based combination therapy (artesunate plus clindamycin) in the treatment of African children with uncomplicated falciparum malaria . In conc